uca.eduuca.edu/geography/files/2015/08/using-gis-and-remote... · web viewusing gis and remote...

USING GIS AND REMOTE SENSING TO FACILITATE HUMAN EXPOSURE

ESTIMATION AND AGRICULTURAL PESTICIDE APPLICATION

by

XXXX XXXXXXX

Thesis ProposalDepartment of Geography and the Graduate School of University of Central Arkansas

Master of Geographic Information Systems

Conway, Arkansas Spring – 2015

Draft Thesis Proposal

TABLE OF CONTENTS (Partial)

CHAPTER 1 – INTRODUCTION

A. Background……. ………………………………………………………………………... 1

B. Statement of the Problem………….. ……………………………………………………. 2

C. Significance of the Problem …………..…………………………………………………. 3

D. Statement of the Purpose ………… …………………………………………………...... 5

E. Theoretical/Conceptual framework .……………… ………………………………......... 6

F. Objectives, Hypothesis & Research Questions ...…………….. ……………………….... 9

G. Assumptions/Theoretical Limitations …………….. ……………………………………11

CHAPTER 2 – LITERATURE REVIEW

A. Agricultural Pesticide Risk in Environmental Health …………………………………...12

B. Using GIS for Estimating Human Exposure to Environmental Health Hazards ………. 15

C. Remote Sensing in Agriculture and Exposure Estimation ..……………………………. 19

D. GIS Based Dispersion Modeling for Evaluating Exposure Risk ………………………. 23

E. Summary ……………………………………………………………………………….. 25

CHAPTER 3 – METHODOLOGY

A. Population and Sample ………………………………………………………………… 29

B. Setting ………………………………………………………………………………….. 31

C. Measurement Methods ………..………………………………………………………... 32

D. Plan for Data Collection ...……. ….……………………………………………………..34

E. Plan for Data Analysis .………………………………………………………………….35

F. Limitations ……………………………… …………………………………………….. 38


Chapter 1

INTRODUCTION

A. Introduction/Background

In the fields of environmental and spatial epidemiology (an integration of techniques from

the disciplines of geographic information systems (GIS) and environmental health), promising

new approaches have emerged to study and respond in an effective manner to detrimental

environmental health impacts. For example, due to their emphasis on space and location, the

application of geospatial information technologies such as GIS and remote sensing are uniquely

suited to the investigation and analysis of environmental health hazards and to the minimization

of environmental contamination. Additionally, since agriculture is inherently a spatial and

temporal activity, the use of geospatial technologies by farmers are providing tangible benefits

by minimizing operational risks and increasing harvest production.

This study focuses on the use of geo-technologies for examining the geographical

relationship between pesticides used on crops grown near rural and peri-urban elementary

schools within the Central Valley of California. Proximity to crops and residential exposure to

agricultural pesticides have been the subject of numerous studies with results indicating the

presence of increased pesticide concentration adjacent to rural residential neighborhoods

(Maxwell, Meliker, & Goovaerts, 2010).

Additionally, elementary schools proximate to agricultural fields are known to be directly

vulnerable to high levels of environmental hazards such as pesticide spray drift, which could

potentially increase the risk of adverse health effects in children (Owens & Feldman, 2004).

However, there appears to be a paucity of research and lack of environmental health

1


investigations in connection with school proximity to industrial agricultural pesticide application

(Wishtoyo Foundation, 2003). Therefore, a primary emphasis of this paper is to investigate the

integration of geospatial related technologies to environmental health research concerning

agricultural pesticide use near public elementary schools, and to recommend improvements for

supporting agricultural pesticide application and exposure assessment.

B. Statement of the Problem

Some of the most restrictive policies in the nation for limiting the use of agricultural

pesticides near schools are in California (California Environmental Health Tracking Program

[CEHTP], 2014). County policies related to application of agricultural pesticides of public health

concern near elementary schools are primarily intended to prevent risks of acute pesticide

exposure, not risks of chronic pesticide exposure (i.e., repeated exposure over a long period of

time, even in minor amounts). Even though many regulatory policies dictate pesticide

applications to be conducted before and after ordinary school hours, many agricultural pesticides

or their byproducts may remain in the environment after they are applied. Because of this

chemical persistence near vulnerable populations, there may be implications for chronic

exposure risks (also known as chronic or persistent effects) and delayed adverse health outcomes

in children (Roberts & Karr, 2012). Therefore, school children proximity to agricultural chemical

hazards and the possibility of disproportionate or mis-specified environmental health impacts

due to risks of spray drift and persistent low-level exposure from agrochemicals is a problem

facing many rural and peri-urban schools in the Central Valley.

In addition to potential pesticide exposure risk near school populations, identifying the

location of pesticide applications on crops through yearly county section-level (Public Land

2


Survey System) maps is a geographic scale too coarse or not optimal for exposure analysis

(Maxwell, Airola, & Nuckols, 2010). Researchers also note that comprehensive state land use

maps that can be used to link pesticide use to specific crop fields are temporally intermittent

(e.g., produced every 5-7 years) and do not adequately identify idle or multi-cropped fields and

crop field extent (Maxwell, Airola, & Nuckols, 2010). Consequently, a common problem that

current agricultural focused exposure models encounter is that they do not consider pesticide

applied to all crops grown over the entire year and/or properly delineate the crop field exposure

boundaries which may underestimate or overestimate modeled pesticide assessment.

Ultimately, as a means to better assess the use of pesticides near sensitive populations,

such as elementary school children, improved efforts for monitoring of agrochemical

applications and exposure assessment are needed. Better knowledge of the problems dealing with

agricultural pesticide use using geo-technologies can improve information and understanding for

evaluating ways to minimize potential exposure from industrial agricultural pesticide application

near public school sites.

C. Significance of the Problem

The proximity of schools to pesticide use puts children at risk of exposure to airborne

pesticides in many California agricultural areas (CEHTP, 2014). Studies have shown that much

of the occurrence of pesticides in the atmosphere can be attributed to agricultural use because of

the large acreage involved and the large chemical quantities used (Majewski & Capel, 1995).

Drift is the unintentional airborne movement of pesticides to non-target areas such as residential

areas, schools, and other spaces (Harrison, 2011). Atmospheric pesticide inputs typically occur

during the agricultural application process (e.g., spraying) through evaporation and drift, and

3


post-application through volatilization and wind erosion. Volatilization is a common pathway for

pesticides to enter the environment (Majewski & Capel, 1995). Because of environmental

conditions, volatilization transpires when pesticide surface residues change from a solid or liquid

to a gas or vapor after application (Gao, et al., 2012). Consequently, volatilization of pesticides

from agricultural fields composes a large source of potential human exposure with some

pesticides having up to a 90% volatilization rate (Harnly, et al., 2005). For example, pesticides

such as heptachlor and trifluralin when surface applied can have 90% loss by volatilization

within six or seven days (Majewski & Capel, 1995).

In some observational epidemiological studies, chronic health outcomes in school-age

children may be associated with persistent, low-level or sub-acute pesticide exposure over time

(Roberts & Reigart, 2013). Pesticide exposure, whether from acute poisoning or

persistent/chronic effects, may induce chronic health complications in children, including

neurodevelopmental or behavioral problems, birth defects, asthma, and cancer (Roberts & Karr,

2012). Children are particularly vulnerable and at risk to the adverse health effects of pesticide

exposure due to their size, their rapidly growing bodies, and the special ways they physically

interact with their school environment and other students such as spending more time outdoors,

playing on the ground, and putting objects in their mouths (CEHTP, 2014).

The magnitude of the problem is also characterized by the need for environmental health

researchers to have accurate, unbiased exposure estimates since teachers and especially children

are often unaware they have been exposed to pesticides during the school year (Oregon Toxics

Alliance [OTA], 2008). Moreover, an important weakness of many epidemiological studies is

adequacy and reliability of exposure assessment (U. S. Environmental Protection Agency [EPA],

4


2013). As expressed by Maxwell (2011), measuring pesticide exposures directly are often

expensive, difficult, or impossible to acquire, especially for retrospective studies where data are

collected only after enrollment (i.e., post-diagnosis), and for this reason, epidemiologists are

employing GIS technology to estimate pesticide exposures.

Finally, since crops planted can change from year to year, more precise crop maps for every

year are needed to accurately identify more precise locations for pesticide use. Identifying more

accurate crop fields and boundary conditions at the time of pesticide application utilizing satellite

images with high resolution (30 meters) could potentially improve prediction of chemical

movement in the environment, minimize the application of agricultural chemicals, and fine-tune

pesticide exposure estimates (Maxwell, Airola, & Nuckols, 2010). For these reasons, using GIS

and RS image time series to characterize crop management practices has the ability to enhance

the adequacy and reliability of pesticide exposure assessment.

D. Statement of the Purpose

The study’s purpose consists of two goals. The first goal is to investigate using geospatial

technology which industrial agricultural areas in the county are in close proximity to public

elementary school sites, and have the most potential for spray drift onto school property with

potential exposure to pre-adolescent children. As part of this geospatial investigation, attention

will be focused on agrochemical spatial distribution and possible off-target transport of

atmospheric pesticide clouds near public elementary schools. This initial examination involves

using a combination of geo-technologies and public data from the California Pesticide Use

Reporting (PUR) system, U. S. Census Bureau, County of Kern and the California Department of

Education for estimating potential public school children (K – 8th) exposure. The second goal is

5


to explore the potential for using satellite imagery and GIS to support pesticide exposure

assessment and agricultural operations in Kern County by evaluating temporal (multi-year)

variability of agricultural crop characterization for a select sample of major farm commodities.

As part of this examination, crop signatures validated or supported by California Department of

Water Resources (CDWR) land use (crop) survey maps will be integrated into a crop signature

library for future use in performing crop field matching and estimating crop maps in non-CDWR

land use survey years for the study area. This research involves integrating data from CDWR, the

California Pesticide Use Reporting (PUR) system and public domain satellite and aerial imagery

for Kern County.

E. Theoretical/Conceptual Framework

The U.S. Environmental Protection Agency (EPA) is the federal agency whose goal is to

safeguard public health and the environment from environmental stressors which are any

biological, physical, or chemical agent that can potentially lead to adverse environmental and

health impacts to ecosystems and humans (Zartarian & Schultz, 2010). EPA’s legislative

authority for protecting human health and the environment from unintended negative effects of

chemical and toxic substances stems from federal legislation such as the Federal Insecticide,

Fungicide, and Rodenticide Act of 1947 and the Toxic Substances Control Act of 1976 (U. S.

Environmental Protection Agency [USEPA], 1988). For this agency to meet its mission it has

established a general framework for exposure science and research (U.S. Environmental

Protection Agency [USEPA], 2009).

The USEPA has defined that vulnerability within the exposure research framework refers

to characteristics of a receptor (e.g., an individual, population or ecosystem) that places them at

6


increased risk of an adverse effect by a chemical, physical, or biological stressor (USEPA, 2009).

For evidence of an exposure, there needs to be contact (e.g., air, soil, water, food) between the

stressor and the receptor in both space and time (Zartarian, Bahadon, & McKone, 2005).

Typically, this involves contact with the visible exterior of a person such as the skin, and body

openings such as orifices and lesions (USEPA, 2009).

The patterns of pesticide exposure take the form of either direct or indirect routes. A direct

exposure typically occurs to persons who personally apply pesticides in agricultural,

occupational, or residential settings, and an indirect exposure is likely to occur through water,

air, dust, and food and represents routes of long-term, generally low-level exposures (Alavanja,

Hoppin, & Kamel, 2004).

A pesticide may be a chemical or biological substance used for preventing, destroying, or

repelling pests such as insects, animals and weeds by interfering with their metabolic processes

(Alavanja, Hoppin, & Kamel, 2004). Environmental hazard assessments in intensive agricultural

practices are profoundly shaped by the use and application of pesticides near vulnerable, receptor

populations. The three major types of agrochemicals that contribute to atmospheric pesticide

contamination are herbicides, insecticides, and fungicides, and the atmosphere is considered as

the major pathway by which pesticides are transported and deposited in off-target areas

(Majewski & Capel, 1995). Figure 1 is an example of the receptor-based approach used in

exposure science which looks at different contaminants and concentrations that reach people

(National Research Council, 2012). In this source-to-outcome framework, GIS technologies can

be used in many steps from geographically identifying receptors exposed to agents in time and

7


space, to mapping agent sources, analyzing environmental intensity, and displaying patterns of

adverse health outcomes.

Figure 1: Selected scientific and technologic advances considered in relation to the conceptual framework. Adapted from “Exposure Science in the 21st Century: A Vision and a Strategy,” by National Research Council, 2012, Washington DC: The National Academies Press. Copyright 2010 by the American Psychological Association. Reprinted with permission.

One common trait in environmental epidemiological analysis and GIS investigations is the

necessity to assign environmental exposure values from current and accurate databases to the

receptor population for their eventual use in geospatial analysis (Boscoe, Ward, & Reynolds,

2004). The need to geocode and link exposure estimates drawn from one or more geographic

data sets to persons represented by some other spatial value is critical for reliable health research

and spatially based investigations (Goldberg, Jacquez, & Mullan, 2013). Utilizing a GIS decision

support approach in the environmental exposure assessment provides improvements in relation

to the conceptual framework such as the flexibility in the terms of the number and types of

exposure data the model can employ, the diversity of geographic objects that can be used with

the spatial intersection operations, the complex operations that can be utilized to generate

8


exposure estimates based on disparate criteria, and the array of options of combining per-area

spatial exposures into a single exposure for the individual (Goldberg, Cockburn, & Naito, 2013).

In addition to using GIS technology for tracking pesticide sources, concentrations and human

receptors, remote sensing (RS) has emerged as a vital technology in human health exposure

research. A primary contribution to the conceptual framework stemming from RS is its ability to

provide real-time information that can be used for crop management during the course of a

growing season. Examples of types of RS image analysis includes identifying crop field

boundaries and condition, crop classification, crop yield, and biophysical characterization

(Nellis, Price, & Rundquist, 2009). The environmental characterization of single and multiple

crop areas using RS before and after pesticide applications can enhance the exposure assessment

process and provide real-time knowledge for minimizing potential exposure effects within the

guidelines of the EPA source-to-outcome framework.

In short, the source-to-outcome framework reflects that chemical and biological agents are

primary stressors of concerns near sensitive and vulnerable populations, and human exposure

may differ based on various factors such as receptor population density, degree and source of

environmental intensity, and receptor time-activity and behavior. The geo-technology component

of this framework aids decision makers in many ways by ascertaining: geographical areas of

exposure, types of source contamination, the agrochemical fate and transport, environmental

hazard concentration, crop area characterization; and more effective agricultural surveillance

strategies.

F. Objectives, Hypothesis & Research Questions

9


The primary objective of this study is to improve scholarly understanding of pesticide

exposure from common farm chemical species applied to agricultural crops near sensitive and

vulnerable populations. This will be conducted by investigating industrial agricultural

applications near public elementary schools in the vicinity of Kern County, California by

estimating potential chemical exposure based on proximity to school sites and the integration of

crop specific land use coverage and PUR data for geospatial and agrochemical diffusion analysis.

The purpose of this objective is to develop a geospatial methodology for evaluating the

environmental risk of harmful agrochemical substances released from industrial agricultural

acreage in close proximity to public elementary schools. The research for this objective will

address the following questions. Which public elementary schools in a given year have had

agrochemicals of public health concern applied within the geography of risk zones, and which

school sites may have had human exposure to industrial agricultural pesticide applications?

The secondary objective is to determine the feasibility of integrating CDWR land use maps

(over 70 different crops or crop categories) and satellite data to derive Kern County crop maps

located in the vicinity of public elementary schools in time periods where public maps

identifying actually planted or cultivated crops are not generally available, which will allow for

appreciable improvements to chemical exposure estimation. The ensuing geo-technology

analysis will be used to mitigate the gaps in exposure assessment found in the spatial

epidemiological literature, specifically as it relates to exposure misclassification and crop

pesticide applications near schools. The purpose of this objective is to demonstrate the feasibility

of GIS and RS to provide improved agricultural surveillance and spatial resolution refinement

for pinpointing pesticide application areas (catchment delimitation) and land cover condition at

10


the crop field level. The research for this objective will address the following questions. Does

geospatial technology allow for integrating intermittent CDWR land use maps and satellite data

to derive crop maps in locations and time periods where intermittent state crop maps are

generally not available, and for downscaling crop-specific CPUR data from section-level to the

crop field-level?

G. Assumptions/Theoretical Limitations

The geographic scale for the study is focused at the valley portion of the county and school

site boundaries, and not necessarily at the individual census tract level. These geographies are

assumed as the appropriate scale of analysis for this study. The Euclidean distances used in this

study assume direct and/or indirect exposure to industrial agricultural pesticides primarily from

atmospheric deposition and inhalation by receptors (elementary school children). Pesticide

application restrictions for Kern County prevent applications of restricted materials (restricted

materials are pesticides deemed to have a higher potential to cause harm to public health, farm

workers, domestic animals, honeybees, the environment, wildlife, or other crops compared to

other pesticides) within a ¼ mile of schools that are in session or during school sponsored

activities when children are present (CEHTP, 2014).

11


Chapter 2

LITERATURE REVIEW

A. Agricultural Pesticide Risk in Environmental Health Research

Over the past decade, California’s multibillion-dollar irrigated agriculture has become more

environmentally sustainable with its regulatory leadership, reporting and state’s pesticide laws

formulated to reduce pesticide risks. However, problems relating to application of agricultural

chemicals and resulting drift residues still occur. Large-scale pesticide drift incidents have

transpired with disturbing regularity in recent years, frightening and sickening thousands of

Californians near agricultural fields (Harrison, 2011).

The problematic use of pesticides is firmly emphasized by Caroline Cox (1995), who

maintains that the management of pesticides threatens the quality and health of the environment

and is an ethical issue that farmers must confront. The costs of chemical poisoning and the

diversity of pesticide problems from aerial and ground applications are staggering, and spray

drift incidents over the years have subjected enormous numbers of people to toxic hazard

exposure without their consent and often without their knowledge.

Agricultural pesticide use in California is a common practice throughout the state’s

agricultural areas (Californians for Pesticide Reform, Pesticide Watch, and Center for

Environmental Health [CPR], 2010). The state’s high-value agriculture illustrates an industrial

approach to cropland production, which is notably evident in terms of its pesticide use.

California accounts for only 2 to 3 percent of all U.S. farmland, but historically utilizes

approximately 25 percent of the nation’s agricultural pesticides (Harrison, 2011).

12


Pesticide and volatilization drift can occur hours or even days following the initial

application (Majewski & Capel, 1995). When this happens, it can easily spread into the

environment where it can detrimentally affect human health through the contamination of the

atmosphere, soil, and water. Modeling of spray drift suggests that most deposition occurs within

five hundred meters (or about 1/3 mile) of the target application (Nuckols et al., 2007).

Regarding operational application of pesticides, aerial application has decreased due to

governmental restrictions as the potential for spray drift is greater than ground-based boom

sprayers (Matthews, 1999). Majewski and Capel (1995) recognizes that the highest pesticide

atmospheric concentrations usually are connected with locally used agrochemicals and are

seasonal in nature, and during these high-use periods, any regional and long-range atmospheric

inputs are typically insignificant in comparison and lost in the background.

There have been recent risk assessment studies demonstrating the association between

regional agricultural application quantities of organophosphates (toxic bug pesticide) and

measured air concentrations (Harnly et al., 2005). Pesticide volatilization from agricultural fields

constitutes a large source of potential human exposure, especially in children who are

particularly vulnerable to this type of inhalation which can lead to adverse effects in the

neurobehavioral development of fetuses and children (Harnly et al., 2005). Due to a child’s

developing physiological and metabolic systems, scientific evidence indicates that children are

the most susceptible to chemical toxicity from pesticides and the most likely to suffer irreparable

harm from exposure (Meade & Emch, 2010). Additionally, since 1998, numerous case-control,

cohort and ecological studies dealing with pesticide exposure have been published and the

13


majority of them have reported statistically significant increased risks between childhood

pesticide exposure and childhood cancer (Infante-Rivard & Weichenthal, 2007).

Pesticide investigations on the impacts of human health depend on good measures of

exposure to study health outcomes such as cancer. Zartarian et al. (2005) describes exposure

assessment as a process that determines the magnitude, frequency and duration of exposure to an

agent, along with the number and characteristics of the population exposed. This process is

normally a component of an overall human health risk assessment which is defined as the

characterization of the potential adverse health effects associated with human exposures to

chemical hazards (Asante-Duah, 2002).

Two important observations for environmental health investigations regarding large-scale

pesticide applications are that exposures may be from inhalation, dermal contact with soil or

dust, or ingestion via food or drinking water, and long-term health effects of residential

exposures to large-scale pesticide application is important to understand because it may identify

preventable causes of disease (Brody et al., 2002). Direct measurement of external pesticide

exposure from the air or from biological markers in the body are common methods for deriving

metrics for quantification of dose (Alavanja, Ward, & Reynolds, 2007). Moreover, bio-

monitoring or the measurement of pesticide metabolites in urine samples collected over multiple

24-hour time periods is considered the gold standard approach for estimating human exposure to

pesticides and other environmental contaminants (Meeker et al., 2005).

A common concern with human bio-monitoring is the fact that individuals usually do not

self-report pesticide exposures (Brody et al., 2002). Another concern is that environmental and

biological exposure measurements are expensive, and if there is noncompliance with study

14


protocols, population bio-monitoring may provide inadequate exposure assessment or

misclassification of dose concentration during the etiologic period of disease (Fortin, Carrier, &

Bouchard, 2008). Consequently, environmental assessments of possible long-term health effects

from exposures to historical large-scale pesticide use from agricultural applications in populated

land use areas pose unique difficulties. Faced with these assessment challenges, epidemiologists

often have relied on geographical proximity as a basic exposure surrogate indicating potential

contamination from chemically treated agricultural land without consideration for influencing

factors such as meteorology or topography.

B. Using GIS for Estimating Human Exposure to Environmental Hazards

As part of the geospatial technology approach to estimating pesticide exposure, a central goal

in spatial and environmental epidemiology research is to identify and describe locations of

populations at risk for increased exposure to a suspected environmental hazard that may lead to

an adverse health outcome (Cromley & McLafferty, 2012). The basic exposure assessment

process for quantifying pesticide exposures includes the following steps: defining the study

population, characterizing temporal variability in pesticide use patterns (especially in the event a

receptor (individual) was potentially exposed to more than one active ingredient), determining

the source of exposure which includes comprising the rate and method of application, and

identifying key factors of human exposure such as the distance from the person’s residence to the

chemical hazards (Nuckols et al., 2004; Franklin & Worgan, 2005; Lu et. al., 2000).

Different geographical contexts exist when analyzing environmental hazards with GIS for

exposure modeling. Cromley and McLafferty (2012), contributed to exposure science research

by offering the notion that there is a geographical variation in the distribution of pollution

15


sources and environmental quality, which means that conditions in these zones differentially

expose people to contaminants. Jerrett & Finkelstein (2005) suggest that the geography of risk is

the intersection of the geography of susceptibility (i.e., location of receptor population) and the

geography of exposure (i.e., location of source hazards). Consequently, a geo-technology

approach to agricultural pesticide exposure assessment concerning the geography of exposure

allows for integration of a variety of spatiality variables that can positively impact the estimation

exposure process including: historical reconstruction of pesticide use, current record of

exposures from hazardous chemicals, and crop-specific delineation of agricultural fields.

To facilitate historical research and public access to pesticide use data for health risk

assessments, the California Department of Pesticide Regulation (CDPR) began the PUR system

in 1990, legally requiring growers and applicators to report all agricultural pesticide use (Gunier

et al., 2001).The PUR data consists of active ingredient, pounds of pesticide applied, number of

acres and application date, type of crops farmed, application methods, and location (in square

mile sections) based on the Public Land Survey System (Reynolds et al., 2002). Since the

California PUR system is quite detailed and surpasses almost any other database in determining

the location of pesticide use, this database can be integrated into a GIS with crop maps to

estimate pesticide exposure within a user-specified buffer around a receptor location such as a

residence (Nuckols et al., 2007).

Methods were developed in the early 1990’s to quantify agricultural pesticide use density for

census-block groups using the annual California PUR data and GIS (Gunier et al., 2001). Gunier

et al. (2001) mapped the geographic distribution of pesticide use density by block groups using

the percentiles of the statewide distribution for all probable carcinogens, and indicated the

16


highest use areas were primarily in the Central Valley areas which correspond well with the

heaviest agricultural counties in the state based on farm revenue. Typical GIS steps required for

exposure estimation generally includes: creating catchment areas, obtaining exposure areas

(geographies of risk), calculating exposure per area, and combining each individual exposure

into a total exposure (Goldberg, Cockburn, & Naito, 2013). The generalized GIS processes for

completing many of these basic actions are geocoding, buffering, and intersection spatial overlay

operations.

The PUR data has also been used by researchers Bell, Hertz-Picatto, and Beaumont (2001) to

calculate exposure estimates for a case-control study of pesticides and fetal death due to

congenital anomalies. Bell, Hertz-Picatto, and Beaumont (2001) used two independent data sets

for this analysis. One set contained birth/fetal death certificates obtained from the California

State Vital Statistics Registry, including mother's address, cause of death, data on the pregnancy

and the parents, etc. The second set used California PUR data such as specific chemicals used,

amount applied, date and location for each application. By focusing on the period of fetal

development during the second trimester which is critical to determining the health outcome,

Bell, Hertz-Picatto, and Beaumont (2001) added a more refined approach to the timing of

exposure and vulnerability. This method provides an assessment of the likelihood of exposure

that is independent of the mother's recall and is specific to a time and place.

Rull et al. (2001) and Rull & Ritz (2003) derived a basic modeling approach for historical

exposures from residential proximity to pesticide applications based on the proportion of crop

acres treated, the application rate, and the application frequency. The model utilized two

datasets. One set was pesticide use data from the California PUR system, and the other set was

17


publicly available detailed California county land-use (LU) maps from the California

Department of Water Resources (CDWR). LU maps from CDWR have better resolution than

data maintained by CDPH since PUR data is only recorded at the coarse geographic scale of

approximately 2.6 km2 (one section mile in the PLSS). The LU maps provide better resolution for

identifying the actual spatial geometry of croplands in terms of which crops are grown within the

county, and from these maps which are generated every 5 to 7 years it can be determined which

pesticides were applied. LU maps were used by Rull et al. (2001) and Rull and Ritz (2003) to

increase the credibility of the modeling pesticide exposure estimates by providing finer spatial

and temporal resolution in the spatial overlay operations used for generating exposure estimates.

In addition to spatial overlay operations for estimating exposures, Nuckols et al. (2007)

introduced a novel buffer-based approach around a residence for pesticide exposure calculation

in conjunction with LU crop polygons and CPUR data. The densities used to generate exposure

estimates were calculated as proportions of the total LU polygon sections. Results from this

integrated crop map and CPUR exposure assessment study approach revealed the pesticide use

estimates differ greatly depending on the exposure metric (size of buffer) used and the spatial

scale at which exposure is estimated (Nuckols et al., 2007).

Zandbergen and Chakraborty (2006) advanced the buffer-based analysis by analyzing the

limitations of discrete buffer distances in proximity-based exposure analysis and noted that this

approach could introduce substantial bias in terms of determining the potentially exposed

population, since the results are strongly dependent on the chosen buffer distances which are

frequently arbitrarily designated. The use of multiple discrete buffer distances and cumulative

distribution functions (CDF) provide a more relevant representation of potentially exposed

18


populations, and does not a-priori assume that any particular distance is more pertinent than

another. The CDF research supports the notion that the use of aggregated data or the lack of

individual geocoded locations for potentially exposed populations may not necessarily lead to

uncertainty or error bias in exposure assessment.

C. Remote Sensing in Agriculture and Exposure Estimation

Nellis, Price, & Rundquist (2009) reveals that satellite remote sensing has been used in

agriculture since the 1920’s for crop inventory monitoring. By detecting electromagnetic

radiation, remote sensors can collect remotely sensed data via satellite and aircraft platforms that

can be used for crop condition estimates, yield forecasts, acreage estimates, crop

insect/pest/disease detection, crop biophysical characterization, irrigation management and

precision agriculture (Nellis, Price, & Rundquist, 2009). Remote sensing scientists have

developed vegetation indices (VI) as measures of canopy biophysical properties for evaluating

vegetative covers using spectral measurements (Jones & Vaughan, 2010). These indices

primarily make use of the fact that vegetation displays large differences in reflectance between

the near infrared and the visible bands, and monitoring the reflectance from these bands is one

way that researchers can determine vegetation health.

With public access to satellite imagery data such as Landsat or Aster, researchers can utilize

VI to ascertain the condition of agricultural land cover (e.g., bare soil, vegetation) at or near the

time of pesticide application which can potentially influence pesticide drift reduction practices

(Maxwell, Airola, & Nuckols, 2010). One common type of vegetation index to enhance spectral

response and image appearance is the normalized difference vegetation index (NDVI) which is a

numerical indicator that uses the visible and near-infrared bands of the electromagnetic spectrum

19


for analyzing remote sensing measurements, and for assessing whether the target being observed

contains live green vegetation or not (Jones & Vaughan, 2010). To classify different types of

vegetative cover, researchers generally rely on the use of spectral reflectance libraries for

discriminating between different types of vegetative canopies (Howard, Wylie, & Tiezen, 2013).

Land surface phenology which refers to the spatio-temporal development of the vegetative land

surface as revealed by satellite sensors, can provide metrics based on image time series of VI

such as onset of greening, onset of senescence, and growing season length (Hudson & Keatley,

2010).

Some of the most common satellites ranging in pixel resolutions from 15 meters to 500

meters are the Advanced Very High Resolution Radiometer also known as AVHRR (Satellite –

NOAA), Landsat Enhanced Thematic Mapper Plus also known as ETM+ (Satellite – Landsat 7),

Landsat Operational Land Imagery also known as OLI (Satellite – Landsat 8), Moderate

Resolution Imaging Spectroradiometer also known as MODIS (Satellite –Terra/Aqua), and

Advanced Spaceborne Thermal Emission and Reflection Radiometer also known as ASTER

(Satellite –Terra) (Reed, Schwartz, & Ziao, 2009). With these sensors, various methods of

classifying disparate land covers and uses within a sensed scene has been beneficial in land cover

classification in diverse areas such as agricultural land and the urban environment. Harris (2003)

presents evidence that by using a seven-vegetative-class scheme and the supervised classification

technique different crops could be effectively separated out for revealing agricultural land use

change in Oman.

Ward et al. (2000) demonstrates that remote sensing data and historical records on crop

location can be used to create historical crop maps in a feasibility study using U.S. Department

20


of Agriculture Farm Service Agency records as a source of ground reference data for classifying

a late summer south central Nebraska 1984 Landsat image into crop species within a three-

county area. The feasibility study identified general land cover types (rangeland and bare soil)

and four major crop types (corn, sorghum, soybeans, and alfalfa) with an overall accuracy of

78% for crops and 68% to 96% accuracy for land cover. Ward et al. (2000) revealed that crop

pesticides that were likely to have been applied can be estimated when information about crop-

specific pesticide use is available, and that by using a GIS, zones of potential exposure to

agricultural pesticides and proximity measures can be determined for residences.

Howard et al. (2013) developed a model for classifying major crops by identifying annual

spatial distributions and area totals of corn, soybeans, wheat and other crops over a nine year

period. Ten 250 meter resolution annual crop classification maps were produced for the Greater

Platte River Basin (covering parts of eight agricultural states) and evaluated from 2000 to 2009.

Using remotely sensed data sets from MODIS and other supporting environmental information,

Howard et al. (2013) showed that the model produced crop classification maps that closely

resembled the spatial distribution trends observed in the Federal National Agricultural Statistics

Service (NASS) Cropland Data Layer (CDL), and exhibited a strong statistical association with

county-by-county crop area totals from the U.S. Department of Agriculture census data.

Methodologies for reconstructing historical individual-level exposures to environmental

contaminants were developed for addressing the technical obstacle of linking space-time data

sets by using remotely sensed imagery with land use maps for assisting the study of adverse

health outcomes with long latency (Maxwell, Meliker, Goovaerts, 2010). Maxwell, Airola, and

Nuckols (2010) expanded the use of Landsat satellite data to support chemical exposure

21


assessment in California by defining the phenological characteristic of 17 major crop types and

for validating land use crop maps. The examination focused on the relationship between

pesticides used on crops grown near individual residences and potential adverse health outcomes

by intersecting Landsat time series with CDWR land use maps and selecting field samples to

define the phenological characteristics of major crop groups.

The combination of Landsat 5 and 7 image data benefits pesticide exposure assessment by

providing information on crops field condition at or near the time when pesticides are applied,

and furnishing information for validating the intermittent CDWR map for agricultural counties

(Maxwell et al., 2010). Landsat 8 imagery benefits crop monitoring with an improved duty cycle

that allows collection of a significantly greater number of images per day (Roy et al., 2014).

Using high resolution satellite imagery to ascertain the condition of agricultural land cover (e. g.,

bare soil, vegetation) at or near the time of chemical application is helpful in improving

efficiency of agricultural practices, and minimizing chemical applications and anticipating

potential pesticide drift.

Maxwell (2011) advanced pesticide exposure estimation by using RS and GIS technology to

downscale pesticide use data to crop field level in California instead of through direct bio-

measurement which is often expensive, difficult, or impossible to obtain, especially for

retrospective studies where data are collected only after study enrollment (i.e., post-diagnosis).

This was demonstrated by using a time series of Landsat imagery (resolution 30m) to establish

crop field boundaries and to acquire measures of vegetation greenness over the growing season

for each crop field within the PUR designated Section. The results were compared to a crop

signature library which was used to identify the specific field or fields where paraquat (highly

22


toxic herbicide) was applied to vineyard and cotton fields, thereby allowing for significant

improvements in rural residential-scale pesticide exposure estimation (Maxwell, 2011).

D. GIS Based Dispersion Modeling for Evaluating Exposure Risk

With the exception of persistent pesticides, the greatest pesticide concentration, both in air

and in rainwater, will typically be located in the immediate surroundings of the application area,

and the fate and transport of the pesticides is generally shaped by the meteorology and

physicochemical properties of the pesticide itself (Rathore & Nollet, 2012). Pistocchi (2014)

illustrates that since pesticides are applied once or a few times per year on a specific crop, the

total emission is concentrated in a short period and chemical mass in the environment tends to

disappear as time elapses from the period of emission (emissions or removal rates occur

cyclically in time and at sufficient time intervals for the chemical not to generally accumulate in

the environment). Additionally, Pistocchi (2014) notes that diffusion can be characterized as a

process by which a chemical tends to move from regions at higher concentration to regions of

lower concentration, and in the case of the atmosphere, the chemical is partitioned between a gas

phase and a particle phase.

Since industrial agrochemical pollution is derived from specific sources and usually spreads

out with progressively lower concentrations, showing considerable systematic spatial variation, a

GIS-based approach can be used to model and assess potential levels of exposure to

agrochemicals. Dispersion can often be modeled as a plume that typically shows a normal or

Gaussian distribution of concentration in the vertical and horizontal directions and is appropriate

when modeling in the near field (0-100km), since the plume imparts a lot of information about

23


the distribution of pollutant concentrations in the environment downwind of the source (Maantay

& McLafferty, 2011).

Geospatial analysis of chemical fate and transport data with a GIS may be vector or raster-

based and the correspondence between the distribution of chemicals (concentrations) and other

spatial data may be explored with three possible levels of GIS integration (Chang, 2014). For

example, one way to link GIS with another computer program is a loose coupling which involves

transfer of data fields between the GIS and other modeling (dispersion) programs. Another

approach to integrate GIS with another modeling program is a tight coupling which gives the

GIS and other programs a common user interface. Finally, an embedded system which bundles

the GIS and an extension program with shared memory and a common interface is the third

scenario for coupling GIS with another program.

An example of a loose coupling integration pollution modeling program is AERMOD (air

dispersion (empirical) modeling system) which has been used in conjunction with a GIS to study

air contaminants (Maantay & Maroko, 2009).Typical model data inputs for AERMOD include

source, receptor, site, meteorological, terrain, and dispersion (downwash related information).

An example of a tight coupling integration with GIS for modeling spray drift deposition is

AgDrift (mechanistic modeling program) in conjunction with Spraytran (a combined source-term

and air dispersion model to simulate regional transport of aerially applied pesticides) which has

the ability to assess and compute the downwind drift and deposition of pesticides and the

magnitude of buffer zones needed to protect sensitive receptor populations (Thistle, et al., 2005).

Common model data inputs for Ag Drift and Spraytran includes similar inputs to AERMOD, as

well as flight path, spray nozzles, drop size distributions, and spray material properties. An

24


example of embedded GIS functionality for possible use with chemical pollution modeling

includes ArcGIS extensions such as spatial analyst, geostatistical analyst, and Spray Advisor.

Typical model data sources for using embedded GIS functions for atmospheric pesticide

pollution modeling are source emissions, release height, ambient meteorology, surface area,

vegetation, spray droplet sizes, estimated swath width, elevation/terrain, hydrological and land

use (Pistocchi, 2014; Strager, 2005).

E. Summary

The literature provides support that GIS and RS are well suited for examining spatially

related environmental health issues from many different perspectives. It indicates the necessity

and practicality for using geospatial methodologies in environmental health research, and more

importantly in supporting pesticide application and exposure surveillance/estimation when

dealing with potential adverse health impacts affecting vulnerable populations such as

elementary school children. California stands out as an excellent laboratory for conducting this

type of future research since the state collects monthly PUR data from each county. The PUR

data which is provided by growers and applicators through the County Agricultural

Commissioner (CAC) is uniquely applicable for pesticide health risk assessments (Gunier et. al.,

2001).

The interest for geospatial technologies in environmental epidemiology is emerging based on

the various studies conducted to date that have shown to improve the exposure assessment

process (Zandbergen & Chakraborty, 2006). This interest is also attributed to the capabilities

exhibited in geospatial technologies such as: the integration of multiple data sources (e.g.,

location of pollution sources and population characteristics), representation of geographic data in

25


map form, geostatistical analysis and modeling (e.g., linking exposure with health data for

surveillance), and the application of various techniques (e.g., buffering, overlay) for proximity

analysis (Zandbergen & Chakraborty, 2006).

GIS coupled with diffusion modeling which takes into account meteorological conditions,

characteristics of the emission sources, and the effect of the surrounding built environment is a

logical evolving approach for assessing potential agrochemical exposure. GIS can be used in

combination with dispersion models to simulate the ways in which agrochemical contaminants

propagate in the environment, and maps of iso-risk contours, environmental characteristics and

receptors can be displayed for more refined geospatial analysis (Maantay & McLafferty, 2011).

Moreover, the innovative use of RS by researchers to define phenological characteristics of

major types of crop groups, reconstructing individual-level exposures to environmental

contaminants, and downscaling pesticide use data to the crop field level has created new

opportunities to conduct this type of advanced research without direct human receptor

measurement which is often expensive (Maxwell, 2011).

The exposure science literature reflects that pesticide exposure investigation and assessment

are being successfully undertaken with emerging and innovative geospatial methods within

spatial epidemiological research. However, a major gap in the geospatial health assessment

research literature is the lack of epidemiological information on school proximity to potential

agricultural pesticide exposures and its health effects. There is a need to understand what kinds

of pesticides are being applied near schools and their cumulative health risks, which in turn may

inform future decision-making around school siting, pesticide permitting regulations, or other

policies with the potential to affect public health (CEHTP, 2014). A geo-technology based

26


evaluation method for assessing pesticide exposure near public school sites and minimizing

pesticide spray drift through better crop surveillance/monitoring can help alleviate this gap.

Chapter 3

27


METHODOLOGY

The general strategy for conducting this geospatial study within Kern County, California is

composed of multiple steps. These steps include: 1) identifying a subset of public elementary

school sites with nearby high agricultural pesticide use that are within the geographies of risk

(school sites within discrete buffers/zones are assumed to be associated with potential

agricultural pesticide exposure); 2) identifying groupings of agrochemicals likely to be

environmentally detrimental to children’s health connected to the geographies of risk; and 3)

linking the PUR results with geographically-enhanced school location data (e. g., pounds of

active ingredients of agricultural pesticide potentially applied within the school site boundaries)

including school site demographics (e. g., total number of enrolled students, racial/ethnic

distribution, and percentage of students eligible to participate in the Free and Reduced Price

Meals Program) for modeling and mapping of school populations within the geographies of risk

(the intersection of the location of receptor populations and the location of source hazards). At

least two buffer zones of risk will be used for selecting receptors from school sites for pesticide

dispersion modeling.

Additional actions required for this GIS temporal-spatial study involves using publicly

available Landsat or ASTER satellite-borne imagery in conjunction with CDWR land use

(ground reference crop) maps, PUR data, and aerial photography for the feasibility of deriving

properly delineated crop field exposure boundaries and crop area (land cover) conditions for

facilitating industrial agricultural pesticide application near schools. The RS research conducted

for this portion of the study will cover land characterizing and downscaling of sample crop

populations from at least two distinct growing seasons in non-sequential years.

28


A. Population and Sample

The initial group population for pesticide risk mapping and diffusion modeling will consist of

public elementary school sites with enrolled students during the 2010 school year (kindergarten

through 8th grade) in the valley portion of Kern County. The sample student population and

school sites for agrochemical risk mapping and diffusion prediction will be from schools that had

pesticide active ingredients applied within the geography of restricted zone (1/4 mile or 400 ±

meters). An additional sample of student population and school sites for agrochemical risk

mapping and diffusion prediction will be from schools that had pesticide active ingredients

applied within 2/3 miles (1000 ± meters) of a classroom area since most aerial pesticide

applications can drift between 500 and 1,000 meters and boom-sprayers can drift between 300 to

800 meters (Ward et al. 2000).

Additional geography risk zone parameters, depending on circumstances, may be identified

by discrete buffer analysis using Euclidean distances for determining sample sites/populations

for diffusion modeling. Figure 2 depicts the geographical school boundaries for the county.

29


Figure 2: Kern County Public Elementary School Boundaries

A second population used in this geospatial study will be for demonstrating the feasibility for

downscaling California pesticide use data to the crop field-level which will be composed of

specific Kern County agricultural commodities grown in 1998 and 2006. Using the 1998 and

2006 CDWR land use (crop) maps for Kern County, Landsat or ASTER imagery, and results

from the school proximity risk analysis, the sample population target area will be irrigated crops

that are grown within the geography of risk of a public elementary school that is potentially

exposed to pesticides with active ingredients. A stratified random sample approach will be

undertaken to select about 15 to 30 polygons designated as single cropped or multi-cropped as

representative of the possible crops grown near elementary schools impacted with potential

pesticide drift deposition. Focus will be on polygons at least 200 feet in width and about10 acres

in area and considered a specific crop type (e. g., vineyard, cotton, or citrus). Based on the 1998

and 2006 CDWR crop maps, polygons labeled with a general crop class (e. g., field crop), or

30


labeled as double-cropped, inter-cropped, or mixed land use will not be used. For practical

application and illustrative purposes, a comparable crop characterization analysis and

downscaling of 2010 PUR data to sample crop (field- level) sites near public elementary schools

with potential pesticide exposure will be undertaken using satellite imagery, County of Kern

aerial photography and agricultural maps from the County of Kern Agricultural Commissioner.

B. Setting

Located at the southern end of California’s fertile Central Valley, Kern County is 8,131

square miles of valley, high desert, and mountain communities. Kern County is a leading

petroleum-producing county and consistently ranks among the most-productive agricultural

counties in the United States. Total agricultural production was valued over $6.2 billion in 2012

(Kern County Agricultural Crop Report, 2012) (Figure 3).

31


Figure 3: Study Area - Kern County, CA

In 2011, Kern County had about 851,710 residents. Also, there were about 156 public

elementary schools and 45 middle schools (a total of 201 schools), serving a total of about

119,529 students in 2011 (CAPK, 2013). About 54% of these public elementary schools (109 ±)

are located in the valley portion of the county.

C. Measurement Methods

As part of the procedures and measurement methods for analyzing the potential pesticide

exposure from spray drift on nearby elementary school populations, geo-referencing and geo-

coding will be performed to confirm spatial and demographic characteristics assigned to

elementary school sites which will allow for geography of risk analysis in conjunction with PUR

data. School site boundaries and centroids will be acquired for location accuracy to be used in

conjunction with proximity/buffer and dispersion analysis for determining potential sources of

adjacent industrial agricultural spray drift emissions and zones of risk.

32


Agricultural county parcel, meteorological, topographical and land use data will also be

downloaded for diffusion prediction and spatial overlay analysis. Integrated air dispersion and

spray drift modeling software for predicting near-field spraying deposition and plume buffers in

conjunction with ArcGIS extensions such as spatial and geostatistical analysts will be utilized as

needed for ascertaining the amount of diffusion/concentration of spray deposition to nearby non-

target (school) sites. Key model input characteristics for measuring the degree of potential spray

drift will be determined by topography (terrain, buildings), and application method (ground spray

boom, aerial). In addition, meteorology will be incorporated into the analysis as needed using an

empirical or mechanistic atmospheric dispersion model with data from National Oceanic and

Atmospheric Administration’s (NOAA) Climatic Data Center and its weather and climate toolkit

(independent platform software with output formats compatible with ArcGIS).

For measuring the ability of using satellite data to support pesticide exposure assessment

such as identifying potential pesticide use at the crop field-level, the study will analyze PUR, and

satellite image time series (30m) data, and establish a crop signature library. Since Landsat

imagery is available free of charge and is very robust, it is ideally suited for agricultural and

epidemiological studies requiring frequent measures of land cover conditions at 30 meter

resolution. ASTER imagery is available at no cost (continental U.S. and territories) with similar

high resolution and has ten spectral bands but may be limited in certain instances (not as many

scenes) for historical use (Gao, 2009). Remotely sensed imagery in conjunction with ERDAS

Imagine/IDRISI Selva, and ArcGIS software, will be used to identify and measure sample

cultivated agricultural boundaries and acquire measurements of vegetation greenness over the

growing season for specified crop fields identified at the crop section-level PUR boundary.

33


Satellite imagery typically containing cloud cover will not be used which normally coincides

with winter months in the County such as November through February. NDVI typically derived

from the red visible Band 3 and near infrared Band 4 will be used to measure and characterize

crop phenology. Lastly, to verify satellite image processing results, CDWR land use surveys and

California PUR data for the county will also be used for historical ground reference measurement

of sample cultivated crops and/or crop categories grown in 1998 and 2006.

D. Plan for Data Collection

Selection of school sites will be public elementary schools (kindergarten through 8th grade) in

Kern County. School boundaries, school centroids, and census demographics, are publicly

available from the CDE in a database format and from the county and federal governments in a

GIS format. However, the school point and polygon location data may be occasionally

problematic, attributable to geocoding or address reporting errors. Enhancement of school

boundary data via geocoding addresses and by visual verification and by importing geocoded

points into ArcGIS and overlaid with county assessors’ parcel data is planned. Google Street

View in conjunction with basemap and satellite imagery from Google and Bing as well as

County aerial photography will be used to quality control check school properties and

boundaries. In the event school boundary editing is needed, it will be reviewed in conjunction

with county assessor parcel data before finalizing school property locations.

Publicly available PUR data and agricultural crop information will be electronically retrieved

from the State of California Department of Pesticide Regulation and the County of Kern

Department of Agriculture and Measurement for the years 1998, 2006, and 2010. County street

centerline and parcel boundary data will be digitally acquired from the County of Kern Public

34


Works Department. After school boundary and PUR data are downloaded and finalized for the

study area (valley portion of the county), they will be linked geographically to determine what

kinds and how many pounds of pesticides were applied near public schools that are in the

geography of risk zones. In addition, environmental media data such as meteorological,

topographical/terrain, and land use/land cover, will be collected from public data sources

(county, state, and federal governments) for use in dispersion modeling and buffer analysis of

pesticide deposition for exposure estimation .

Finally, state CDWR land use maps for Kern County will be electronically retrieved for

ground referencing in addition to County aerial photography from the Kern County Council of

Governments. Kern County satellite imagery such as Landsat 5, 7, 8 and/or ASTER images for

the years 1998, 2006 and 2010 will be reviewed and analyzed for possible downloading from the

U.S. Geological Survey (USGS). School data collected in the previous components of this study

will be integrated as needed with imagery data collection for crop map derivation and

characterization. Lastly, the author of this study will be solely responsibility for collecting and

examining this data.

E. Plan for Data Analysis

Modeled exposure rates for relative risk of agrochemical contamination will be analyzed

using GIS in conjunction with dispersion software. Agricultural pesticide use data and respective

school location data will be analyzed using exploratory spatial data analysis with ArcGIS Model

Builder, ArcGIS spatial and geostatistical analyst’s toolset, and SPSS/Excel. Exploratory data

analysis will also investigate for spatial autocorrelation (Moran I), hotpot analysis (Getis-Ord

35


Gi*), cluster and outlier analysis (Anselin local Moran’s I), and spatial pattern (smoothing/trend

surface analysis).

Figure 4 is an evaluation method procedure for analyzing geospatial data for this portion of

the research design.

Figure 4: Dispersion evaluation method procedure

For the agricultural remote sensing analysis of the study, satellite imagery for two respective

time periods (1998 and 2006) will be clipped to the study area boundaries. Specific targeted

school sites with nearby agrochemical emission sources will be merged and examined for

sampling. An unsupervised and/or calculating supervised classification will be conducted on

designated satellite imagery scenes within the agricultural portion (valley) of the county for

certain selected crops. Comparing 1998 and 2006 California DWR's land use (crop) surveys

(GIS format) against satellite imagery for the same time period and location, training samples for

36


a known crop will be identified and collected. Training samples for each respective year for a

sample of specific farm commodities will be analyzed through histograms, scatterplots, and band

statistics. Using an interactive supervised classification the digital mage will be classified

including post-classification processing. Finally, a maximum likelihood classification based on

spectral signatures will be created.

To distinguish between the types of vegetative crops, greenness and brightness of vegetation

will be assessed in the designated RS scenes. The tasseled cap transformation and multiple

NDVI’s will be considered for calculating, analyzing and mapping vegetation and creating a

measure of greenness. NDVI from various study years will be qualitatively and quantitatively

compared using image statistics. Vegetation types will be isolated and NDVI from different crop

types will be compared. Lastly, crop signatures validated or supported by CDWR land use maps

will be integrated into the crop signature library for future use in performing crop field matching

and deriving crop maps in non-CDWR land use survey years. `In addition, validated crop field

signatures will be reviewed for estimating and characterizing land cover condition at the time of

pesticide application which could be helpful in facilitating efficient pesticide application near

schools with the most potential for spray drift onto school property.

Figure 5 is an evaluation method procedure for analyzing geospatial data for this portion of

the research study.

37


Figure 5: Crop field downscale evaluation method procedure

F. Limitations

The pesticide off-target spray/volatilization drift exposure results by discrete buffer/plume

analysis and dispersion prediction are assumed as adequate proxies for estimating potential

exposure to the target population. However, this study does not actually determine if school

children were actually exposed in these areas. Human bio-monitoring of impacted school

populations following approved public health protocols would be the proper mechanism to

validate the actual research findings concerning pesticide risk zones and exposed receptors.

Additionally, the methodology for dispersion modeling had the following limitations: 1)

pesticide transport pathway for this study is generally limited to atmospheric (air), and other

environmental media (e. g., soil, water, ingestion via food, etc.) contamination pathways was not

38


directly programmed into the modeling and analysis; 2) meteorology and source emissions were

simplified with no significant turbulence distance downwind prediction; 3) dry weather

agrochemical application (no pesticide spraying during inclement weather); and 4) generally

smooth (minor relief) surface (valley terrain) agricultural planting schemes for irrigated row

crops.

39


Works Cited

Alavanja, M. C., Hoppin, J., & Kamel, F. (2004). Health effects of chronic pesticide exposure:

Cancer and neurotoxicity. Annual Review of Public Health, 25, 155-197.

Alavanja, M. C., Ward, M. H., & Reynolds, P. (2007). Carcinogenicity of agricultural pesticides

in adults and children. Journal of Agromedicine, 12(1), 39-56.

Asante-Duah., K. (2002). Public Health Risk Assessment for Human Exposure to Chemicals (1st

ed.). Dordrecht, Netherlands: Kluwer Academic Publishers.

Bell, E. M., Hertz-Picciotto, I., & Beaumont, J. J. (2001, September). A case-control study of

pesticides and fetal death due to congenital anomalies. American Journal of

Epidemiology, 148-156.

Boscoe, F. P., Ward, M. H., & Reynolds, P. (2004, December1). Current practices in spatial

analysis of cancer data: data characteristics and data sources for geographic studies of

cancer. International Journal of Health Geographics, 3(28).

Brody, J. G., Vorhees, D. J., Melly, S. J., Swedis, S. R., Drivas, P. J., & Rudel, R. A. (2002).

Using GIS and historical records to reconstruct residential exposure to large-scale

pesticide application. Journal of Exposure Science and Environmental Epidemiology,

12(1), 64-80.

California Environmental Health Tracking Program. (2014). Agricultural Pesticide Use Near

Public Schools in California. Retrieved from http://cehtp.org/page.jsp?page_key=1040

Californians for Pesticide Reform, Pesticide Watch, Center for Environmental Health. (2010).

Pesticide Protection Zones: Keeping Kids Safe at School [Policy Report]. Retrieved from

http://www.pesticidereform.org/downloads/SchoolProtectionZones.pdf

40


Chang, K., (2012). Introduction to Geographic Information Systems (6th ed.). New York, New

York: The McGraw-Hill Companies.

Community Action Partnership of Kern (CAPK). (2013). Head Start & Early Head Start

Program 2013 Community Assessment Update [Annual Report]. Retrieved from

http://www.capk.org/store/files/145.pdf

Cox, C. (1995, Spring). Indiscriminately from the skies. Journal of Pesticide Reform, 15(1), 2-7.

Cromley, E. K., & McLafferty, S. L. (2012). GIS and Public Health (2nd ed.). New York, New

York: The Guilford Press.

Fortin, M. C., Carrier, G., & Bouchard, M. (2008, November 8). Concentrations versus amounts

of biomarkers in urine: a comparison of approaches to assess pyrethroid exposure.

Environmental Health, 7, 55-68. Retrieved from http://dx.doi.org/doi:10.1186/1476-

069X-7-55

Franklin, C. A., & Worgan, J. P. (Eds.). (2005). Occupational and residential exposure

assessment for pesticides. Chichester, UK: John Wiley & Sons, Ltd.

Gao, J. (2009). Digital Analysis of Remotely Sensed Imagery. New York, NY: McGraw-Hill

Companies, Inc.

Gao, J., Wang, Y., Gao, B., Wu, L., & Chen, H. (2012). Pesticides - Evaluation of

Environmental Pollution. H. S. Rathore, & L. M. Nollet (Eds.). Bacon Raton, FL: CRC

Press.

Goldberg, D. W., Cockburn, M. G., & Naito, A. T. (2013). Spatial Overlays. In F. P. Boscoe

(Ed.), Geographic Health Data - Fundamental Techniques for Analysis (pp. 87-104).

Oxfordshire, UK: CABI.

41


Goldberg, D. W., Jacquez, G. M., & Mullan, N. (2013). Geocoding and Health . In F. P. Boscoe

(Ed.), Geographic Health Data - Fundamental Techniques for Analysis (pp. 51-71).

Oxfordshire, UK: CABI.

Gunier, R. B., Harnly, M. E., Reynolds, P., Hertz, A., & Behren, J. V. (2001). Agricultural

pesticide use in California: Pesticide prioritization, use densities, and population

distributions for a childhood cancer study. Environmental Health Perspectives, 109(10),

1071-1078.

Harnly, M., McLaughlin, R., Bradman, A., Anderson, M., & Gunier, R. (2005). Correlating

agricultural use of organophosphates with outdoor air concentrations: A particular

concern for children. Environmental Health Perspectives, 113(9), 1184-1189.

Harris, R. (2003). Remote sensing of agricultural change in Oman. International Journal of

Remote Sensing, 24(23), 4835-4852.

Harrison, J. L. (2011). Pesticide drift and the pursuit of environmental justice (1st ed.). London,

England: The MIT Press.

Howard, D. M., Wylie, B. K., & Tiezen, L. L. (2013). Crop classification modelling using

remote sensing and environmental data in the Greater Platte River Basin, USA.

International Journal of Remote Sensing, 33, 6095-6108.

Hudson, I. L., Keatley, M. R. (2010). Phenological Research: Methods for Environmental and

Climate Change Analysis. New York, New York: Springer

Infante-Rivard, C., & Weichenthal, S. (2007). Pesticides and childhood cancer: an update of

Zahm and Ward’s 1998 review. Journal of Toxicology and Environmental Health, 10,

81-99.

42


Jerrett, M., & Finkelstein, M. (2005). Geographies of risk in studies linking chronic air pollution

exposure to health outcomes. Journal of Toxicology and Environmental Health, Part A,

68(13-14), 1207-1242.

Jones, H. G., & Vaughan, R. A. (2010). Remote Sensing of Vegetation (1st ed.). New York, New

York: Oxford University Press.

Kern County Department of Agriculture and Measurement Standards. (2012). 2012 Kern County

Agricultural Crop Report [Annual Report]. Retrieved from

http://www.kernag.com/caap/crop-reports/crop10_19/crop2012.pdf

Lu, C., Fenske, R. A., Simcox, N. J., & Kalman, D. (2000). Pesticide exposure of children in an

agricultural community: Evidence of household proximity to farmland and take home

exposure pathways. Environmental Research, 84, 290-302.

Maantay, J. A., & McLafferty, S. (2011). Geospatial analysis of environmental health (1st ed.).

New York, New York: Springer.

Maantay, J. A., Tu, J., & Maroko, A. R. (2009, February). Loose-coupling an air dispersion

model and a geographic information system (GIS) for studying air pollution and asthma

in the Bronx, New York City. International Journal of Environmental Health Research,

19(1), 59-79.

Majewski, M. S., & Capel, P. D. (1995). Pesticides in the Atmosphere. Chelsea, Michigan: Ann

Arbor Press, Inc.

Matthews, G. A., (1999). Application of Pesticides to Crops. Covent Garden, London: Imperial

College Press.

43


Maxwell, S. K. (2011, August 25). Downscaling pesticide use data to the crop field level in

California using Landsat satellite imagery: Paraquat case study. Remote Sensing, 3(9),

1805-1816.

Maxwell, S. K., Airola, M., & Nuckols, J. R. (2010, September 16). Using Landsat satellite data

to support pesticide exposure assessment in California. International Journal of Health

Geographics, 9(46), 1-14.

Maxwell, S. K., Meliker, J. R., & Goovaerts, P. (2010, February 25). Use of land surface

remotely sensed satellite and airborne data for environmental exposure assessment in

cancer research. Journal of Exposure Science and Environmental Epidemiology, 20(2),

176-185.

Meade, M. S., & Emch, M. (2010). Medical geography (3rd ed.). New York, New York: The

Guilford Press.

Meeker, J. D., Barr, D. B., Ryan, L., Herrick, R. F., Bennett, D. H., Bravo, R., & Hauser, R.

(2005, September 1). Temporal variability of urinary levels of nonpersistent insecticides

in adult men. Journal of Exposure Analysis and Environmental Epidemiology, 15, 271-

281.

National Research Council. (2012). Exposure Science in the 21st Century: A Vision and a

Strategy. Washington, DC: The National Academies Press.

Nellis, M. D., Price, K. P., & Rundquist, D. (2009). Remote sensing of cropland agriculture. In

T. A. Warner, M. D. Nellis, & G. M. Foody (Eds.), The Sage handbook of remote sensing

(1st ed., pp. 368-380). London, England: SAGE Publications Ltd.

44


Nuckols, J. R., Gunier, R. B., Riggs, P., Miller, R., Reynolds, P., & Ward, M. H. (2007, May).

Linkage of the California pesticide use reporting database with spatial land use data for

exposure assessment. Environmental Health Perspectives, 115(5), 684-689.

Nuckols, J. R., Ward, M. H., & Jarup, L. (2004, June 2004). Using geographic information

systems for exposure assessment in environmental epidemiology studies. Environmental

Health Perspectives, 112(9), 1007-1015.

Oregon Toxics Alliance. (2008). Warning! Hazards to Children - Pesticides in Our Schools.

Retrieved from

http://www.beyondtoxics.org/wp-content/uploads/2011/11/PesticidesInOurSchools_Repo

rtWithMaps_2008.pdf

Owens, K., & Feldman, J. (2004). Getting the Drift on Chemical Trespass [Entire issue].

Pesticides and You, 24(2).

Pistocchi, A. (2014). GIS Based Chemical Fate Modeling (1st ed.). Hoboken, New Jersey: John

Wiley & Sons, Inc.

Rathore, H. S., & Nollet, L. M. (2012). Pesticides: Evaluation of Environmental Pollution (1st

ed.). Boca Raton, Florida: CRC Press.

Reed, B. C., Schwartz, M. D., & Xiao, X. (2009). Remote sensing phenology: status and the way

forward. In A. Noormets (Ed.), Phenology of Ecosystem Processes (1st ed. (pp. 231-246).

London, England: Springer.

Reynolds, P., Behren, J. V., Gunier, R. B., Goldberg, D. B., Hertz, A., & Harnly, M. E. (2002,

February 14). Childhood cancer and agricultural pesticide use: An ecologic study in

California. Environmental Health Perspectives, 110(3), 319-324.

45


Roberts, J. R., & Karr, C. J. (2012, December 6). Pesticide exposure in children. Pediatrics, 130,

1764-1788.

Roberts, J. R., & Reigart, J. R. (2013). Recognition and Management of Pesticide Poisonings.

Retrieved from http://www2.epa.gov/pesticide-worker-safety/pesticide-poisoning-

handbook-table-contents

Roy, D. P., Wulder, M. A., Loveland, T. R., Woodcock, C. E., Allen, R. G., Anderson, M. C., …

Zhu, Z. (2014, April). Landsat-8: Science and product vision for terrestrial global change

research. Remote Sensing of the Environment, 145, 154-172.

Rull, R. P., & Ritz, B. (2003, October). Historical pesticide exposure in California using

pesticide reports and land-use surveys: an assessment of misclassification error and bias.

Environmental Health Perspectives, 111(13), 1582-1589.

Rull, R. P., Ritz, B., Krishnadasan, A., & Maglinte, G. (2001, July 9-13). Modeling historical

exposures from residential proximity to pesticide applications. In: Proceedings of the

Twenty-First Annual ESRI User Conference, San Diego, California.

Strager, J., (2005, October). Spray Advisor. Paper presented at the Deposition Modeling for Risk

Assessment and Decision Support Workshop, Waikoloa, HI.

Thistle, H. W., Teske, M. E., Droppo, J. G., Allwine, J. A., Bird, S. L., Londergan, R. J. (2005,

July). AGDISP as a source term in far field atmospheric transport modeling and near

field geometric assumptions. Paper presented at the American Society of Agricultural and

Biological Engineers Annual Meeting, Tampa, FL.

U.S. Environmental Protection Agency . (2013). Recognition and Management of Pesticide

Poisonings (735K13001). Retrieved from

46


http://www2.epa.gov/pesticide-worker-safety/recognition-and-management-pesticide-

poisonings

U.S. Environmental Protection Agency. (1988). Environmental Progress and Challenges: EPA’s

Update. Berkley, California: University of California Libraries.

U.S. Environmental Protection Agency. (2009). A Conceptual Framework for U.S. EPA’s

National Exposure Research Laboratory (NTIS PB2010-103951). Retrieved from

http://cfpub.epa.gov/si/si_public_record_report.cfm?

dirEntryId=203003&fed_org_id=770&SIType=PR&TIMSType=Published+Report&sho

wCriteria=0&address=nerl/

pubs.html&view=citation&sortBy=pubDateYear&count=100&dateEndPublishedPresent

ed=12/31/2009a

Ward, M. H., Nuckols, J., Weigel, S. J., Maxwell, S. K., Cantor, K. P., & Miller, R. S. (2000,

January). Identifying populations potentially exposed to agricultural pesticides using

remote sensing and a geographic information system. Environmental Health

Perspectives, 108(1), 5-12.

Wishtoyo Foundation. (2003). Agritoxins: Ventura County’s Toxic Time Bomb [Research paper].

Retrieved from http://www.environmentnow.org/pdf/Wishtoyo-Agritoxins-Report.pdf

Zandbergen, P. A., & Chakraborty, J. (2006, May 25). Improving environmental exposure

analysis using cumulative distribution functions and individual geocoding. International

Journal of Health Geographics, 5(23).

Zartarian, V., Bahadon, T., & McKone, T. (2005). Adoption of an official ISEA glossary.

Journal of Exposure Analysis and Environmental Epidemiology, 15, 1-5.

47


Zartarian, V. G., & Schultz, B. D. (2010, June). The EPA’s human exposure research program

for assessing cumulative risk in communities. Journal of Exposure Science and

Environmental Epidemiology, 20(4), 351-358.

48

uca.eduuca.edu/geography/files/2015/08/using-gis-and-remote... · web viewusing gis and remote...

Documents