







































Education, Society and Human Studies 
ISSN 2690-3679 (Print) ISSN 2690-3687 (Online) 

Vol. 4, No. 2, 2023 
www.scholink.org/ojs/index.php/eshs 

14 

Original Paper 

Air Pollution Burden and Three Population Health Indicators in 

58 Counties Comprising the State of California, USA 
Oscar Wambuguh1* & Sofia Lin2 

1 Department of Public Health, California State University East Bay, 25800 Carlos Bee Blvd., Hayward, 

CA 94542, USA 
2 Pre-Professional Health Academic Program, California State University East Bay, 25800 Carlos Bee 

Blvd., Hayward, CA 94542, USA 
* Oscar Wambuguh, Department of Public Health, California State University East Bay, 25800 Carlos 

Bee Blvd., Hayward, CA 94542, USA 

 
Received: August 10, 2023       Accepted: August 20, 2023      Online Published: August 28, 2023 

doi:10.22158/eshs.v4n2p14                       URL: http://dx.doi.org/10.22158/eshs.v4n2p14 

 
Abstract 

Air pollution is a major risk factor in human health causing premature death disease and ranks fifth 

among the top five leading causes of death worldwide. In California, air pollution attracts significant 

attention due to increased pollution from fossil fuel burning, industry, transportation, and the state’s 

drought record and fire-proneness. Data used in this analysis was obtained from the CalEnviroScreen 

pollution monitoring tool. There is significant variation in air pollution across counties and racial 

groups. Hispanic populations, followed by Caucasians, predominantly occupy areas with the highest 

pollution levels above 40%. Most counties had low average scores in population parameters that 

determine population health. Pollution burden predicted asthma and CVD prevalence but not 

low-birth-weight babies. Asthma was closely associated with traffic density, PM2.5, cleanup sites, and 

ozone levels; low-birth-weight babies were associated with traffic density and release of toxic 

substances; and CVD was associated with PM2.5, toxic release, and cleanup sites. Low HPI scores 

(<40%) were associated with incidences of asthma and CVD but not low-birth-weight babies. The 

increased population health compromises in California caused by the effects of air pollution call for a 

paradigm shift in the way population health is evaluated and effects of air pollution mitigated. 

Keywords 

air pollution, CES, pollution burden, HPI, population characteristics, asthma, CVD, low-birth-weight 

babies 

 



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1. Introduction 

Outdoor and indoor air pollution is an established risk factor to human health worldwide and ranks fifth 

among the five leading risk factors for death globally after diet, hypertension, smoking and elevated 

fasting blood glucose levels (GBD, 2017). The air pollutome is a complex mixture of gases (NOx, O3, 

SO2, NH3 and CO), volatile droplets (quinones and polycyclic aromatic hydrocarbons), and primary and 

secondary particulate matter (Pier et al., 2019). In 2017, it was estimated to cause more than 5 million 

deaths worldwide with a reported 2.9 years loss to life expectancy in 2015 (Lelieveld et al., 2020; Santos 

et al., 2021). Early concerns about air pollution focused on industrialization and the use of coal and the 

resulting emission of gases and particles. After World War II, the accelerated development of industries 

and expansion highway systems increased traffic to urban and industrial centers from expanding 

suburbia adding a new dimension to air pollution, one that was less sooty but more chemically reactive 

driven by photochemical transformation of auto exhaust, yielding secondary oxidants that caused eye 

and throat irritation (Stanek et al., 2011). Air pollution increases morbidity and mortality in the general 

population, and studies report that adverse fetal, infant, and childhood growth and development are 

associated with increased risk for disease development in adulthood (Swanson et al., 2009). Ambient and 

air traffic-related air pollution and exposure to different levels of particulate matter (fine [PM2.5], coarse 

[PM2.5-10], and large [PM>10]) in young children leads to lower health outcomes with increased 

cardiovascular morbidity, asthma development, wheezing, respiratory infections, allergies, and adverse 

neurodevelopmental effects (Gheissari et al., 2022). Globally, WHO reports that 93% of children under 

the age of 15 years live in environments with higher than the recommended levels of environmental air 

pollutants (WHO, 2018). Dondi et al. noted that even low levels of air pollution can affect children’s lung 

function and growth and increase the frequency and incidence of respiratory diseases such as asthma, 

bronchiolitis, respiratory infections, and bronchitis (2023). 

Air pollutants have been implicated in cancer etiology, premature mortality, asthma, chronic obstructive 

pulmonary disease, cardiovascular disease; aggravate existing chronic conditions like type-2 diabetes; 

and raise risks of Alzheimer’s disease and dementia (Apte et al., 2015; Erickson & Jennings, 2017; 

Erickson, 2017; Liu et al. 2021). Unfortunately, the ambient air pollution disease burden is not equitably 

distributed across individuals, communities, countries, regions, or demographic groups. Past studies 

indicate higher-than-average air pollution exposures for racial/ethnic minority populations and 

lower-income populations in the United States leading to disparities in attributable health impacts (Liu et 

al. 2021). Historically, the immigrant status, race and ethnicity have been closely associated with 

economic opportunity, education access, affordable housing, food security, transportation, and healthy, 

safe environments (Mir et al., 2013; Matsui et al., 2020). Race and ethnicity have been found to influence 

health through the harmful effects of discrimination, racism, exclusionary policies, and segregation 

(Morello-Frosch et al. 2001; Balazs et al., 2011). In California, air pollution is a big public health concern 

as there are various types of pollutants coming from the burning of fossil fuels, and industrial and 



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transportation activities. Air monitoring shows that over 90 percent of Californians breathe unhealthy 

levels of one or more air pollutants during some part of the year. These pollutants chiefly include ozone, 

PM2.5 and PM10, oxides of nitrogen (NOx), carbon monoxide, oxides of sulfur (SOx), lead, and hydrogen 

sulfide (ARB, 2023).  

In the United States, air pollution was associated with ~100,000 annual premature deaths in 2017 

(Stanaway et al., 2018). The Emission Inventory Improvement Program (EIIP) was established in 1993 

by the United States Environmental Protection Agency (EPA) to promote the development and use of 

standard procedures for collecting, calculating, storing, reporting, and sharing air emissions data. It was 

designed to promote the development of emission inventories that have targeted quality objectives, are 

cost-effective, and contain reliable and accessible data for end users (EPA, 2023). The EIIP inventory and 

materials are available to states and local agencies, the regulated community, the public, and other 

stakeholders. Although California outdoor air quality has made tremendous improvements in the last 2-3 

decades, the pollution reduction and resulting health and environmental benefits are not uniformly 

distributed across the state, within a region, or among all population segments (CalEnviroScreen, 2023). 

In the state of California, a pollution monitoring tool (CalEnviroScreen) was developed to analyze air 

pollution levels and human health effects in 58 counties in the state of California. The California 

Communities Environmental Health Screening Tool (CalEnviroScreen 4.0) released in October 2021, is 

the latest iteration of the California Communities Environmental Health Screening Tool. It was 

developed following consultation with government, academic, business, and nongovernmental 

organizations and 12 public workshops in 7 regions of the state that resulted in more than 1000 oral and 

written comments on 2 preliminary drafts (Alexeeff & Mataka, 2014). The CalEnviroScreen tool 

purposefully relies on publicly available data sets for transparency and relatively simple methods so that 

it can be understood by a general audience (Cushing et al., 2015). The CalEnviroScreen original and 

subsequent versions were developed by CA’s Environmental Protection Agency’s (CalEPA) Office of 

Environmental Health Hazard Assessment to evaluate the cumulative existence of multiple pollutants 

and stressors in communities (OEHHA, 2023). 

The Health Places Index (HPI) is a project of the Public Health Alliance of Southern California, a 

coalition of the executive leadership of 10 local health departments in Southern California, representing 

more than 60% of the state’s population. The HPI maps data on social conditions that drive population 

health (e.g., education, employment, clean air/water, neighborhood conditions, social resources, and 

health care access) composed of 25 indicators organized into eight policy domains. The overall HPI score 

is a sum of weighted domain scores, with the economy and education taking the lion’s share (50% of total 

weights). Such social determinants of health indicators are often used by research institutions, policy 

makers and other stakeholders to evaluate health and well-being of communities, identify health 

inequities and quantify factors that influence health (Healthyplacesindex.org, 2023). The HPI is a tool 

that ranks communities at the census-tract level based on factors known to shape health outcomes 



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(Maizlish et al., 2019). The HPI was designed to help prioritize marginalized and disadvantaged 

communities in public policy and investments. It is composed of various social determinants of health 

indicators with weighted community-level attributes, and gives economic factors the most weight while 

housing, healthcare access, and environmental exposures are given less weighted (Cleveland et al., 

2023). 

Many communities in California experience a disproportionate burden of pollution from local sources 

like traffic and industry, but also from dispersed pollution in multiple media including air, water, soil, and 

farm produce. Most of these communities experience the additional socioeconomic stressors and health 

conditions that make them more vulnerable to the impacts of pollution (CalEnvironScreen, 2023). To 

address the cumulative effects of both pollution burden and these additional factors, and to identify which 

communities might need policy, financial, educational, or programmatic interventions, OEHHA 

developed and maintains and updates the CalEnviroScreen tool on behalf of CalEPA. Ferguson et al. use 

the term triple jeopardy to refer to the three-tiered conundrum facing low-socioeconomic status (SES) 

communities. They include exposure to greater environmental hazards, increased susceptibility to poor 

health outcomes due to pre-existing health burdens (e.g., chronic stress, poorer health status, less 

opportunity to choose health-promoting behaviors), and resultant health disparities across SES groups 

(2021).  

The current study sought to explore and analyze both the CalEnviroScreen 4.0 and HPI data to explore 

how pollution levels in the state: a) varied across the 58 California counties; b) varied across the state’s 

racial groups and age categories; c) how pollution burden was associated by three indicators of health 

(asthma, low-birth-weight babies and cardiovascular prevalence); d) how individual pollutants were 

associated with people’s health; and lastly, e) how the HPI was associated with three population health 

indicators (prevalence of asthma, low-birth-weight babies, and cardiovascular conditions). 

 

2. Method 

2.1 CalEnviroScreen 4.0 Model 

The CalEnviroScreen model evaluates the cumulative pollution burden and vulnerabilities in California 

on a geographical basis. The model considers 21 indicator variables in its assessment for each of 

California’s approximately 8000 census tracts. These indicators are divided into two main categories 

(pollution burden and population characteristics) and four subcategories (exposures, environmental 

effects, sensitive populations, and socioeconomic factors) as shown in Table 1. Each of the indicators 

was quantitatively measured for each census tract and was converted to a percentile rank relative to all 

other California census tracts. For each census tract, the score of each subcategory was determined by 

taking a mean average of the percentile values of its indicators. Pollution Burden was calculated by 

taking a 2:1 weighted average of exposures and environmental effects, respectively. Population 

Characteristics was calculated by taking a 1:1 average of sensitive populations and socioeconomic 



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factors. Lastly, the CES 4.0 score for each county was calculated by applying min-max normalization 

on a scale of 0 to 10 for Pollution Burden and Population Characteristics separately and then 

multiplying them for a total CES score out of 100. 

2.2 Data Transformation 

The data used in this study comes from three publicly available data sources: CalEnviroScreen 4.0 tool, 

the CalEnviroScreen Race Analysis tool, and the California Health Places Index 3.0 tool. Statistical 

analysis was performed on these datasets using R (version 4.2.2) and using the native analysis packages 

that belong to base R. For each of the three sources, all census tracts were grouped by county that they 

belong to. For each of the 58 California counties, a mean average was taken for all reported metrics. 

This analysis done in the current study is performed on this county-level overview of the data. 

 

Table 1. Components of CalEnviroScreen 4.0 Model 

Pollution Burden Population Characteristics 

Exposures Environmental Effects Sensitive Populations Socioeconomic Factors 

Ozone concentrations Cleanup sites Asthma emergencies Housing-burdened low-income 

PM2.5 concentrations Groundwater threats (ER visits) households 

Diesel PM emissions Hazardous Waste Cardiovascular Disease Linguistic Isolation 

Drinking H2O contaminants  Impaired water bodies (ER visits for heart attacks) Poverty 

Lead risk from housing Solid waste sites Low-birth-weight infants Unemployment 

Pesticide use   Educational attainment 

Toxic releases from facilities    

Traffic impacts    

 

2.3 Statistical Analysis 

Using the three main metrics of the CalEnviroScreen model (Pollution Burden, Population 

Characteristics, and combined CES 4.0 score), we define counties to be high risk in that category if the 

county has a score greater than the 40th percentile. Using demographic breakdown for each county 

provided by the Race Analysis report, age group and race/ethnicity profiles were generated 

independently for the group of counties above and below the 40th percentile threshold for each of the 

three metrics. Race was reported by the CalEnviroScreen tool as seven categories: Caucasian, Asian 

American, Native American, African American, Hispanic, and other/multiple. Age groups were defined 

as under 10 years old, 10-64 years old, and over 64 years old. To determine how HPI is associated with 

the three indicators of community health (asthma, low-birth-weight-babies, and cardiovascular disease 

prevalence), a simple linear regression t-test was performed. Likewise, the association of each of the 

three health indicators with HPI and with Population Characteristics were independently investigated in 

the same manner.  



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To further study these associations and how individual factors were associated with people’s health, 

multiple regression analysis was also performed on the group of indicator variables that make up the 

pollution burden and population characteristic scores. For pollution burden, the three health indicators 

were tested against eight pollutants (traffic volume, PM2.5, ozone, toxic releases, pesticides, diesel 

particulate matter, hazardous waste, and hazardous cleanup sites). For population characteristics, the 

three health indicators were tested against five socioeconomic factor indicators (education, housing 

burden, linguistic isolation, poverty, and unemployment). To investigate possible associations between 

individual indicator variables, we performed a series of spearman correlation tests. The correlation of 

poverty with each of the other four socioeconomic factor indicators was studied as well as the 

correlations of unemployment with either linguistic isolation or education level. Lastly, individual 

correlation tests were performed on CES 4.0 score with each of the six reported race/ethnic groups. 

 

3. Results and Discussion 

The distribution of CES scores for all counties in the state are shown below (Fig. 1). Most of the counties 

(24) had CES percentile scores over 40%, 19 had scores between 31-40%, and 15 had CES scores less 

than 30%. Excluding population characteristics (health conditions, education, language barriers, 

employment, poverty level and housing), the pollution burden percentile rankings had 18 counties over 

40%, 18 between 31-40%, and 22 below 30% (Fig. 2). As noted by Cushing et al. (2015), there is an 

uneven geographic distribution of the CES percentile scores across the state. California population 

distribution is skewed and concentrated in a few major urban regions: Sacramento, Fresno, Bakersfield, 

San Francisco, San Diego, and Los Angeles. Only 35.7% of California cities met the World Health 

Organization (WHO) target for annual PM2.5 exposure of 10 μg/m3, as compared to the national average 

of 81.7% (IQAir, 2023). Consequently, the top five cities with reported worst PM2.5 and ozone levels in 

the country are in California and include Los Angeles-Long Beach, Visalia, Bakersfield, 

Fresno-Madera-Hanford, San Francisco-San Jose. In 2019, 11 of the 15 most polluted US cities were 

located within 50 miles of Los Angeles, while best air quality was found in the state’s more sparsely 

populated interior cities, where vehicular and industrial emissions are relatively sparse, and wildfires are 

infrequent (IQAir, 2023). Information analyzed in this study also indicates that the 24 CES highest 

percentile scores greater than 40% are in those primarily urban counties that comprise of Los Angeles 

(67%), Alameda (12%), and Fresno (Fig. 1). Those with scores less than 40% were topped by San Diego 

(21%); Solano/Sonoma (17%) and both Santa Barbara and Santa Clara (12.5% each). Although some of 

those lower than 40% areas have major cities like San Diego and San Jose, it appears that a significant 

number of census tracts included in the database are away from the urban centers, and therefore have 

relatively cleaner and better-quality air. 

 



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Fig. 1. Distribution of CES Percentiles Rankings in California 

 

 
Fig. 2. Distribution of Pollution Burden Percentiles Rankings in California 

 

Combined race profiles for the state shows that CES percentile scores greater than 40% were 

predominantly highest in counties occupied by Hispanic populations (44.2%), followed by Caucasian 



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(32%), Asian American 13.9% and African American 6.6%. Areas occupied by other races (Native 

Americans, multiple, and others) had low CES percentile scores of 3% or below. CES percentile scores 

less than 40% were highest in areas occupied by Caucasian populations (47.9%, followed by Hispanic 

(28.4%), Asian American 16.1% and African American 3.4% (Fig. 3a, 3b).  

Although racial and ethnic diversity in California is high, it often is associated with distinct 

socioeconomic disparities, health disparities and disproportionate burdens of environmental pollution 

(Conroy et al., 2018). Mathiarasan and Huls noted that the intersection of both outdoor and indoor air 

pollution with socioeconomic conditions can result in many adverse health outcomes including 

respiratory (asthma, bronchitis, infections, and sleep disordered breathing), neuropsychological 

(developmental delays, structural alterations in the brain, slower working memory, and mental health 

issues), and a myriad of other health outcomes (2021). As stated by Bell et al., public health 

professionals, policy makers and healthcare practitioners consider racial health inequities to be crucial in 

understanding health outcomes in people of color (2020). In California, about 44% of the people in the 

state speak a language other than English at home (versus national: 21.6%) with 28% of people speaking 

Spanish, and 9.8% Asian and Pacific Islander languages. In 2020, approximately 39% of the population 

living in California were Hispanic, 35% Caucasian, 15% Asian or Pacific Islander, and 4% African 

American (PPIC, 2023). California has the largest population of Hispanics and the third largest percent 

Hispanic population nationwide. Comprising a total of 45% of California’s population of 39 million 

residents, Southern California has a higher percentage of Hispanics with approximately 10.6 million 

Hispanic residents, (US Census Bureau, 2023). California has a 12.3% poverty rate (versus national: 

12.8%) with a median household income of about $84,907 (versus national: $69,717). Regarding 

educational attainment, only about 36.2% of the population have a bachelor’s degree or higher, and 

around 21% have a high school diploma (data.census.gov, 2020). The results in this study that people of 

Hispanic origin dominate in areas with a higher pollution burden (44.2%) than Caucasians and other 

traces (Fig. 3). These findings seem consistent with previous studies indicating that communities of color 

across the United States often live in places with worst air quality, more environmental hazards, and 

fewer health promoting environmental amenities like parks, trails, and open space (Cushing et al., 2015; 

Huang et al., 2018). In California, people of color experience more adverse health conditions (like 

asthma, cancer, bronchitis, preterm births, and cardiovascular disease) due toxic air contaminants from 

industry, live closer to hazardous waste sites and traffic, and in areas with significant water pollution 

(Balazs et al., 2011; Morello-Frosch et al., 2001). Studies from the northeastern United States report that 

the annual particulate matter (both PM10 and PM2.5) was consistently higher in lower socioeconomic 

areas compared to areas of high socioeconomic areas; and that PM2.5 concentrations were associated 

racial composition and were higher in areas with racial minorities (Brochu et al., 2011). Race and 

socioeconomic status are reported to be closely associated with a disproportionate level of disease in 

populations of color and many of the disparities in access to healthcare (Mathiarasan & Huls, 2021).  



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a) CES >40%          b) CES <40% 

Fig. 3. Combined Race Profiles of CES Percentiles Rankings in California 

 
Combined age profiles for CES percentile scores greater or less than 40% are very similar. Most people 

living in those areas were respectively between 10-64 years of age (73.9% and 72.8%), seniors 64 years 

(13.2% and 15.6%), and children 10 years and under (13% and 11.6%) (Fig. 4a, 4b). The population of 

children in this study (11.6-13%) is small but significant because children are still in their early body and 

organ developmental stages making them more vulnerable to the effects of air pollution. Both intrinsic 

and extrinsic factors increase the vulnerability and/or susceptibility of individuals to the adverse effects 

of air pollutants. Past panel studies of children with and without asthma have noted that child lung 

function is a subclinical marker of acute pollution health effects, with short-term decrements in lung 

function observed with short-term increases in ambient air pollution. Short-term increases in air pollution 

are related to increases in adverse clinical events including ED or urgent care visits and hospitalization 

for respiratory illnesses (Spektor et al., 1988; Raizenne et al., 1989). Long-term childhood air pollution 

exposures have been demonstrated to likely shift the entire population distribution of childhood lung 

function and lung function growth downward (Garcia et al., 2021). 

 

 

 



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a) CES >40%          b) CES <40% 

Fig. 4. Combined Age Profiles of CES Percentiles Rankings in California 

 
Studies on older adults report that this age-group is especially vulnerable to hazards in their immediate 

environment; and traffic-related pollution exposure has been shown to result in worse cognitive function 

among those living in more polluted areas (Ailshire & Clarke, 2015). Of particular concern to population 

health is when fine particulate matter (PM2.5) is inhaled, the small particles are reported to cause damage 

to organs such as the brain (Peters et al., 2006). In a study of its kind, demonstrating an association 

between air pollution and cognitive function in a racially diverse sample of older U.S. men and women, 

living in areas with higher levels of PM2.5, Weuve et al. showed that there was more rapid cognitive 

decline in the adults 55+ years over a 2-year period (2012). Markers of neuroinflammation and 

neuropathology associated with neurodegenerative conditions such as Alzheimer’s disease have been 

linked to living in areas with high levels of air pollution especially of PM2.5 (Peters et al., 2006). In this 

study, older adults were a significant proportion of the state’s population making 13-15.6% of the total 

(Fig. 4a, 4b). With advancing age, pre-existing chronic disease in older adults (like asthma, COPD, 

pulmonary fibrosis, arrhythmias, hypertension, ischemic heart disease, diabetes, autoimmune diseases, 

and obesity) makes them more vulnerable to the combined health effects of air pollution. Older adults are 

also likely to experience immune-senescence, progressive decline in lung function, and greater 

incidences of hospitalizations due to air pollution exposure (Santos et al., 2021). 

 



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Fig. 5. Distribution of Population Characteristics Percentiles Rankings in California 

 
The distribution of population characteristics (health conditions, education, language barriers, 

employment, poverty level, and housing) in California show that a slight majority of the counties (57%) 

had lower population percentile averages than the rest (Fig. 5). The relationship between asthma, 

low-birth-weight babies and CVD levels and population characteristics (poverty, race, housing, linguistic 

isolation, and unemployment) was also investigated. Multiple regression indicates that only poverty and 

linguistic isolation were significant for asthma; none of the characteristics were significant for 

low-birth-weight babies; and CVD was predicted by housing conditions, unemployment, and linguistic 

isolation, but only marginally associated with poverty levels (Table 2). 

 

Table 2. Probability Values between Population Characteristic Percentiles and three Health 

Indicators 

Population characteristic Asthma Low-birth-weight babies CVD 

Poverty 0.038* 0.09 0.051 

Linguistic isolation 0.033* 0.77 0.02* 

Unemployment 0.28 0.33 0.01* 

Housing burden 0.41 0.82 0.02* 

*Indicates significance 

 

 



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Life course characteristics like socioeconomic status, level of education, existing health conditions, and 

employment contribute to differential air pollution exposure levels/risk and increases the susceptibility of 

people living in those conditions. Such susceptibility can be brought about by poor health status, 

addictions, other pollutant exposure, family history, psychological stress, and poor nutrition. Cushing et 

al. found that in California, there was significant evidence that cleanup and solid waste sites, 

concentrations of ozone and diesel particulate matter, and pesticide use are disproportionately located in 

communities with higher levels of poverty (2015). Several studies in China found that people with lower 

socioeconomic status normally experienced a higher health risk from air pollution, while people in 

middle or high socioeconomic levels had lower risks presumably because they had more ways and means 

to mitigate air pollution (Jiao et al., 2018). However, research on how life course characteristics influence 

the health effects of air pollution produced mixed results (Jiao et al., 2018 provides a good review). As 

part of the socioeconomic status of an individual, a study of 20 cities in the United States reported that 

individual education significantly modified the relationship between coarse particulate matter (PM10) and 

mortality—specifically, the higher education level of the individual, the lower the effect of PM10 on 

mortality in (Zeka et al., 2006). People of low socioeconomic status usually lack a good education, and 

therefore lack the knowledge regarding health and environmental pollution in addition to the health 

effects caused by air pollution. As a result, they are more likely to have a lower level of awareness and 

means of self-protection (Jiao et al., 2018). Laurent et al. (2007) provided an alternative argument that the 

modification effect of socioeconomic status on air pollution health effects depended on the regional level 

at which socioeconomic characteristics were measured. When socioeconomic characteristics were 

measured at coarser geographical resolutions (like city, regional or statewide), there was no modification 

effect. When measured at community level, the results were mixed, but when individually measured 

socioeconomic characteristics were used, the result indicated disadvantaged people tend to be more 

affected by the effects of air pollution. Several reasons make people of low socioeconomic status more 

vulnerable to the effects of air pollution: most are in relatively poor health due to lack of resources, have 

poor diet, live in overcrowded households, have poor access to health services, are employed in jobs that 

expose them to more outdoor and indoor air pollution, and live in areas with less opportunities for 

physical activity and exercise. 

A simple regression analysis of: a) pollution burden and asthma prevalence indicates a significant 

relationship [F (1,56) =4.71, p=0.034, Fig. 6a]; b) pollution burden and low-birth-weight babies 

prevalence (Fig. 6b) indicates no significant relationship [F (1,56) =2.57, p=0.115], and c) pollution 

burden and cardiovascular disease (CVD) prevalence (Fig. 6c) indicates a significant relationship [F 

(1,56) =5.67, p=0.021]. 

 



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Fig. 6a. Relationship between Pollution Burden and aAsthma Prevalence (p=0.034) 

 

 

Fig. 6b. Relationship between Pollution Burden and Low-birth-weight Babies Prevalence 

(p=0.115) 

 



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Fig. 6c. Relationship between Pollution Burden and Cardiovascular Disease (CVD) Prevalence 

(p=0.021) 

 

Among the eight pollution level percentile indicators reported on the CalEnviroScreen database (ozone, 

PM2.5, diesel PM, pesticides, toxic release, traffic, clean-up sites and hazardous waste) we were interested 

in finding out which pollutants were mostly associated with the prevalence of asthma, low-birth-weight 

babies, and CVD. Multiple regression analysis suggests that asthma was closely associated with traffic 

density, PM2.5, cleanup sites, and ozone levels. There was no association between asthma and diesel PM, 

toxic release, hazardous waste, and pesticide levels. For low-birth-weight babies, only two indicators 

were associated: traffic density and toxic release. CVD was only associated with three indicators: PM2.5, 

toxic release and cleanup sites (Table 3). 

 

Table 3. Pollution Level Percentile Indicators versus Asthma, Low-birth-weight-babies, and CVD 

Prevalence 

Pollution Factor P-values 

 Asthma Low-birth-weight-babies CVD 

Traffic density 0.02* 0.018* 0.07 

PM2.5 0.002* 0.58 0.001* 

Cleanup sites 0.007* 0.33 0.04* 

Diesel PM 0.19 0.128 0.49 

Hazardous waste 0.09 0.56 0.48 

Ozone 0.04* 0.41 0.47 



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Toxic release 0.28 0.033* 0.05* 

Pesticides 0.94 0.25 0.52 

*Indicates significance 

 

With reference to particulate matter levels reported in this study, McConnell et al., more than twenty 

years ago studying 3,676 children from 12 locations in the state of California, showed that children with 

asthma who were exposed to air pollution from three pollutants (NO2, PM10, and PM2.5) had a higher 

prevalence of respiratory symptoms and a greater need for medication than did children without asthma 

(1999). Current research indicates that there is a consistent relationship between exposures to ambient air 

pollution or traffic-related air pollution and childhood asthma (or wheeze) in early-childhood (Zhu et al., 

2017). The development of eczema and allergic symptoms in children has been closely associated with 

postnatal exposure to ambient PM10, NO2, and O3 (Liu et al., 2020). Several studies demonstrated that the 

risk of respiratory infections (e.g., pneumonia, rhinitis, or bronchitis) in infants and children was 

associated with increased short-term exposure to ambient PM10, O3, NOx, and SO2 (Gheissari et al., 2022). 

Particulate matter, especially fine (PM2.5), can penetrate deep into the lower respiratory tract, escapes 

host defense and alveolar clearing mechanisms, and may reach the bloodstream and organs (including the 

placenta and the brain) through translocation across biological membranes (Mannucci et al., 2019). The 

effects of air pollution appear to be more marked during the first years of life, including during the 

intrauterine period when mothers inhale air pollutants.  

Although no significant relation was found in this study between pollution burden and low-birth-weight 

babies, specific single factors like the levels of traffic pollution and toxic substances released in the air 

were associated with asthma prevalence. Research on prenatal exposure to ambient air pollution 

(especially PM2.5, O3, NOx, and SO2) has consistently been associated with reduced or low-birth-weight 

babies across various populations and geographic locations (Ebisu et al., 2012; Vinikoor-Imler et al., 

2014). Gheissari et al. report that birth weight is inversely correlated with prenatal exposures to certain 

chemical constituents present as pollutants in air (like zinc, sulfur, elemental carbon, silicon, titanium, 

and aluminum) (2022). In areas in California with significant wildfire smoke, its exposure has been 

associated with preterm birth and low-birth-weight babies. Expectant women who lived in wildfire-prone 

areas were at a greater risk of preterm birth or low newborn birth weight than those living in non-wildfire 

zones (Abdo et al., 2019).  

Epidemiological studies indicate that increased pollution from wildfire smoke is linked to increased 

cardiovascular events (hypertension, angina, and cardiac arrest) particularly among the elderly. 

Proximity to wildfire has strongly been associated with increased respiratory symptoms, medication use, 

or hospital visits in adults (Martin et al., 2013). Most regions in California suffer from significant air 

pollution from wildfires especially north of Sacramento, central Valley, and southern regions (Los 

Angeles/San Bernardino mountains) are frequent wildfire zones. Studies in ambient and traffic-related 



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air pollution have linked air pollution exposures in neonates and children with increased cardiovascular 

morbidity (Ghessari et al., 2022). Experimental studies have demonstrated that increased morbidity 

through inflammation, oxidative stress, and transcription regulation can result from early-life air 

pollutant exposure. During pregnancy maternal inflammatory cytokines induced by air pollutants have 

been found to cross the placenta and induce fetal inflammation and oxidative stress that can last through 

childhood (Tillett, 2012; Gheissari et al., 2022). Abnormal immune profiles in newborns and children 

exposed to air pollution in utero have been reported in recent studies (Black et al. 2017). Early postnatal 

exposure to air pollutants like PM2.5 can generate reactive oxygen species (ROS) and inflammatory stress 

in addition to inducing developmental dysfunction and epigenetic alterations (Møller et al., 2014). 

In this study cardiovascular disease was also associated with three specific pollution indicators (PM2.5, 

cleanups, and toxic release). Several studies have explored the relationship between air pollution and 

cardiorespiratory diseases, but from a global perspective less appears to be known about the health risks 

across regions and populations. The air pollution—health relationship is greatly complicated by “effect 

modifiers” such socioeconomic status, level of education amongst individuals, energy use, sources of 

pollution, distances from polluting sources, and prevailing weather conditions during peak pollution 

times of day among others (Requia et al., 2018). Long-term air pollution studies have projected mixed 

opinions in the relationship between particulate matter (PM2.5) and increased risk of all-cause mortality, 

and significant risks in both cardiopulmonary and cardiovascular disease (Lipsett et al., 2011). Atkinson 

et al. also showed that associations for respiratory causes of death were larger than for cardiovascular 

causes. For example, one study reported that for PM10, each increment of 10 micrograms per cubic meter 

was associated with an increase in obstructive pulmonary disease mortality in China, United States, and 

the European Union (Song et al., 2014). A relationship was also reported between heart failure and 

increases in CO, SO2, NO2, PM10, PM2.5 (Shah et al., 2013). Another study stated that a 10 microgram per 

cubic meter increment in PM2.5 was associated with a small increase in the risk of death for 

cardiorespiratory diseases (Atkinson et al., 2014). Lastly, Requia et al. reported a small increase in 

hospital admissions and mortality attributed to cardiorespiratory diseases per 10 microgram per cubic 

meter increment in PM2.5 (2018).  

Although the actual microgram levels of particulate matter in this study were not available, nevertheless, 

the study found that incidences of asthma and cardiovascular disease could be predicted from the CES or 

pollution burden percentile scores. This observation provides excellent opportunities for the 

implementation of pollution reduction intervention strategies by policy makers, industry, educators, 

research agencies, non-profit organizations, community activists, and local citizens in improving 

environmental quality in areas with high CES percentile scores. Considering that communities living in 

such areas are not likely (or are unable) to relocate, interventions might include educating populations on 

ways to reduce individual exposure to pollutants, especially for children and older individuals. This can 

be achieved using social media, cell phone text alerts when air quality is poor, distribution of fliers, 



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billboards and posters, community activism, and community education sessions. As noted by Gheissari 

et al., determining and identifying detrimental air pollutants from the pollution exposure mixture and 

finding out the detrimental chemical components within those pollutants (in situations where specific 

drivers of toxicity are identifiable), will help formulate community intervention targets and pollution 

exposure reduction strategies in high CES score areas (2022). 

Most counties in California (about 30) had a HPI higher than 40%, and the rest (26) had a HPI lower than 

40% (Fig. 9). By race category, almost equal numbers of Caucasian (38%) and Hispanic (35.5%) 

populations live in areas with higher HPIs (40-100%), and so do 17.1% of Asian Americans. In areas 

with lower HPIs (10-39%) about half of the people are of Hispanic origin (49.8%), about 34.8% are of 

Caucasian heritage, and 7.1% Asian Americans (Fig. 10 a, b). The relationship between Healthy Places 

Index (HPI) and prevalence of asthma, low-birth-weight babies, and CVD was tested and was found to be 

predictive. In areas where the HPI falls below 40%, asthma and CVD prevalence were significantly 

higher [F (1,54) =19.29, p<0.001; F (1,54) = 55.39, p<0.01 respectively] but it was not the case for 

low-birth-weight babies [F (1,52) =1.54, p=0.22] (Fig. 11a,b,c).  

 

 
Figure 9. Distribution of Healthy Places Index Percentiles in California Counties 

 



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a) HPI >40%                                    b) HPI <40%. 
Figure 10. Combined HPI Percentile Profile for California by Race 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



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Fig. 11. Relationship between Healthy Places Index (HPI) Scores and three Health Indicators 

a) Asthma b) Low-birth-weight babies c) CVD 

 

a) p<0.001 

b) p=0.22 

c) p<0.01 



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Consistent with what was found in this study where half of Hispanics in California live in regions with 

low HPIs, Cleveland et al. observed a similar pattern for 10 counties in Southern California—counties 

with higher proportions of Hispanics were associated with lower HPIs (2023). The study also found that 

cities with a higher proportion of Hispanic residents had higher rates of adult and childhood obesity and 

adult diabetes cases and lower HPI percentile scores. The HPI score was found to explain the highest 

proportion of the variability in the percentile of adult obesity, adult diabetes, and childhood obesity, 

which underlines the fact that social determinants of health are strongly associated with poor metabolic 

health. Cleveland et al. also found that a lower HPI score was strongly associated with poor physical and 

mental health, lower life expectancy, and a greater prevalence of smoking, asthma, and heart attacks 

(2023). It is therefore no coincidence in this study that areas where the HPI scores fell below 40% were 

predictive of the prevalence of asthma and CVD in those communities. In California, communities of 

color are likely to live near areas with significant traffic and diesel particulate matter, toxic releasing 

industries that use fossil fuels, hazardous waste generating and processing sites, less open space lacking 

developed areas for physical activities like walking, bicycling, and other park activities. Such 

communities are also likely to be less educated, have little or no health insurance, their children walk or 

use public transport to get to school, and may have coexisting health conditions that increase their 

vulnerability to air pollution-related detrimental health effects. Living conditions in low socioeconomic 

areas cause social and psychological stress, which makes the body more susceptible to infections and 

diseases; stress also increases the risk of developing negative health outcomes with exposure to air 

pollution (Mathiasaran & Huls, 2021). Williams reported that socioeconomic status and race are closely 

tied together, and they can be attributed to disproportionate levels of disease in populations of color and 

many of the disparities in access to healthcare due to racism (1999).  

The state of California currently focuses on reducing pollutant emissions to meet policy mandates in the 

face of climate change. There is a glaring lack of risk management policies related to air pollution 

resulting in people of lower socioeconomic (for example, children, seniors >65 years of age, and people 

with existing health conditions) failing to receive the related public services. The formulation of public 

policy to mitigate the negative impact of air pollution exposure becomes critical for the regions with 

more serious air pollution. Public policy makers must advocate and lobby the government to rev up 

financial investment that will provide protective and air purification equipment, and information 

consulting services for marginalized populations. The government must also continue improving existing 

health care policies, enhance access to essential and appropriate medical services to the disadvantaged 

populations, and minimize the health effects arising from air pollution. 

 

4. Conclusion 

With the climate change predictions projected this century, disadvantaged and minority residents in many 

parts of California will continue to face escalating levels of both outdoor and indoor air pollution that will 



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continue to affect their health and well-being. Climate change will intensify dry conditions in fire-prone 

California causing more intense fires, toxic release, and smoke inhalations. It will also increase the risk of 

disease due to changing and extreme weather patterns, which are also associated with the increase in 

allergens as well as disease vectors. These conditions will greatly raise the prevalence of respiratory 

conditions and diseases, such as asthma. Disadvantaged communities will be hit hardest with fewer 

available resources, are less educated, lack healthcare access, subsist on poor nutrition, face aggravated 

stress levels due to poor living conditions, and have less ability to evacuate from affected areas. Since so 

many people’s health is compromised by the effects of air pollution in California, there needs to be a 

paradigm shift in the way population health is evaluated and effects of air pollution mitigated. We need 

more interdisciplinary research to understand the complexity of the in-built environments where 

marginalized people live, and the synergistic interactions in many environmental phenomena and how 

they affect people’s health. More information is also needed to identify specific ways that cumulative 

impact assessment can be most effectively used to reduce environmental inequalities. We need viable and 

actionable solutions like increased investment in education, infrastructure, and in the physical spaces 

where people live. We need policies and practices that promote urban revitalization, modifying the built 

environment in ways that create enterprising zones, shifting of available resources (like job creation and 

food outlets), assessing the impacts of the social determinants of health, expanding urban housing, and 

capacity building through deliberate community engagement. The lives of marginalized communities 

will need better focused approaches from national, regional, and local governments, private corporations, 

private entrepreneurs, educational institutions, and the community at large to deconstruct the 

socioeconomic dilemmas facing them, characterizing issues of racism and discrimination, leveling of 

environmental injustices experienced, and addressing rampant health disparities and inequities. 

 

Acknowledgement 

Data used in this study is publicly available at the California’s Environmental Protection Agency’s 

(CalEPA) Office of Environmental Health Hazard Assessment website. California Communities 

Environmental Health Screening Tool: CalEnviroScreen 4.0, released in October 2021. Accessed on 

May 17, 2023, at https://oehha.ca.gov/ calenviroscreen/report/ calenviroscreen-40. 

 

 

 

 

 

 

 

 



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