Mapping wild vascular plant species diversity in urban areas in California using crowdsourcing data by regression kriging: Examining socioeconomic disparities.

Biodiversity is crucial for human health, but previous methods of measuring biodiversity require intensive resources and have other limitations. Crowdsourced datasets from citizen scientists offer a cost-effective solution for characterizing biodiversity on a large spatial scale. This study has two aims: 1) to generate fine-resolution plant species diversity maps in California urban areas using crowdsourced data and extrapolation methods; and 2) to examine their associations with sociodemographic factors and identify subpopulations with low biodiversity exposure. We used iNaturalist observations from 2019 to 2022 to calculate species diversity metrics by exploring the sampling completeness in a 5 × 5-km2 grid and then computing species diversity metrics for grid cells with at least 80 % sample completeness (841 out of 4755 grid cells). A generalized additive model with ordinary kriging (GAM OK) provided moderately reliable estimates, with correlations of 0.64-0.66 between observed and extrapolated metrics, relative mean absolute errors of 21 %-23 %, and relative root mean squared errors of 27 %-30 % for grid cells with ≥80 % sample completeness from 10-fold cross-validation. GAM OK was further applied to extrapolate species diversity metrics from saturated grid cells (N = 841) to the remaining grid cells with <80 % sample completeness (N = 3914) and generate diversity maps that cover the grid. Further, generalized linear mixed models were used to examine the associations between species diversity and sociodemographic indicators at census tract level. The wild vascular plant species diversity metrics were inversely associated with neighborhood socioeconomic status (i.e., unemployment, linguistic isolation, educational attainment, and poverty rate). Minority populations (i.e., African American, Asian American, and Hispanic) and children had significantly lower diversity exposure in their neighborhoods. Crowdsourcing data offers a cost-effective solution for characterizing large-scale biodiversity in urban areas.

Assessing Perception of Wildfires and Related Impacts among Adult Residents of Southern California.

Major wildfires and their smoke pose a threat to public health and are becoming more frequent in the United States, particularly in California and other populated, fire-prone states. Therefore, it is crucial to understand how California residents view wildfires and engage in risk-reducing behaviors during wildfire events. Currently, there is a knowledge gap concerning this area of inquiry. We disseminated a 40-question cross-sectional survey to explore wildfire perception and knowledge along with related risk-reducing measures and policies among 807 adult residents in the fire-prone region of Orange County, California. Results demonstrated that nearly all (>95%) participants had (or knew someone who had) previously experienced a wildfire. Female gender, knowing a wildfire victim and reporting to have a general interest/passion for environmental issues were the three factors most strongly associated with (1) wildfires (and smoke) being reported as a threat, (2) participants' willingness to evacuate if threatened by a nearby wildfire, and (3) participants' willingness to support a wildfire-related tax increase (p < 0.05). The majority (57.4%) of participants agreed that the occurrence of wildfires is influenced by climate change, with the most commonly reported risk-reducing actions (by 44% of participants) being informational actions (e.g., tracking the news) rather than self-motivated physical safety actions (e.g., using an air purifier) (29%). The results of this study can help to inform decision- and policy-making regarding future wildfire events as well as allow more targeted and effective public health messaging and intervention measures, in turn helping to reduce the risk associated with future wildfire/smoke episodes.

Community-Engaged Use of Low-Cost Sensors to Assess the Spatial Distribution of PM2.5 Concentrations across Disadvantaged Communities: Results from a Pilot Study in Santa Ana, CA.

PM2.5 is an air pollutant that is widely associated with adverse health effects, and which tends to be disproportionately located near low-income communities and communities of color. We applied a community-engaged research approach to assess the distribution of PM2.5 concentrations in the context of community concerns and urban features within and around the city of Santa Ana, CA. Approximately 183 h of one-minute average PM2.5 measurements, along with high-resolution geographic coordinate measurements, were collected by volunteer community participants using roughly two dozen low-cost AtmoTube Pro air pollution sensors paired with real-time GPS tracking devices. PM2.5 varied by region, time of day, and month. In general, concentrations were higher near the city's industrial corridor, which is an area of concern to local community members. While the freeway systems were shown to correlate with some degree of elevated air pollution, two of four sampling days demonstrated little to no visible association with freeway traffic. Concentrations tended to be higher within socioeconomically disadvantaged communities compared to other areas. This pilot study demonstrates the utility of using low-cost air pollution sensors for the application of community-engaged study designs that leverage community knowledge, enable high-density air monitoring, and facilitate greater health-related awareness, education, and empowerment among communities. The mobile air-monitoring approach used in this study, and its application to characterize the ambient air quality within a defined geographic region, is in contrast to other community-engaged studies, which employ fixed-site monitoring and/or focus on personal exposure. The findings from this study underscore the existence of environmental health inequities that persist in urban areas today, which can help to inform policy decisions related to health equity, future urban planning, and community access to resources.

Use of Radioisotope Ratios of Lead for the Identification of Historical Sources of Soil Lead Contamination in Santa Ana, California.

Lead (Pb) is an environmental neurotoxicant that has been associated with a wide range of adverse health conditions, and which originates from both anthropogenic and natural sources. In California, the city of Santa Ana represents an urban environment where elevated soil lead levels have been recently reported across many disadvantaged communities. In this study, we pursued a community-engaged research approach through which trained "citizen scientists" from the surrounding Santa Ana community volunteered to collect soil samples for heavy metal testing, a subset of which (n = 129) were subjected to Pb isotopic analysis in order to help determine whether contamination could be traced to specific and/or anthropogenic sources. Results showed the average 206Pb/204Pb ratio in shallow soil samples to be lower on average than deep samples, consistent with shallow samples being more likely to have experienced historical anthropogenic contamination. An analysis of soil Pb enrichment factors (EFs) demonstrated a strong positive correlation with lead concentrations, reinforcing the likelihood of elevated lead levels being due to anthropogenic activity, while EF values plotted against 206Pb/204Pb pointed to traffic-related emissions as a likely source. 206Pb/204Pb ratios for samples collected near historical urban areas were lower than the averages for samples collected elsewhere, and plots of 206Pb/204Pb against 206Pb/207 showed historical areas to exhibit very similar patterns to those of shallow samples, again suggesting lead contamination to be anthropogenic in origin, and likely from vehicle emissions. This study lends added weight to the need for health officials and elected representatives to respond to community concerns and the need for soil remediation to equitably protect the public.

Use of historical mapping to understand sources of soil-lead contamination: Case study of Santa Ana, CA.

This paper investigates the historical sources of soil-lead contamination in Santa Ana, California. Even though dangerous levels of soil-lead have been found in a wide variety of communities across the United States, public health institutions lack clarity on the historical origins of these crises. This study uses geo-spatial data collected through archival research to estimate the impact of two potential sources of lead contamination in the past -- lead-paint and leaded gasoline. It examines, through a combination of statistical and historical methods, the association between lead concentrations in contemporary soil samples and patterns in the evolution of the city's physical features, such as the growth of urbanized areas and the historical flow of traffic. We emphasize the value of historical data collected through archival research for understanding the sources of environmental lead, particularly leaded gasoline, which our study found to be the most likely and most prominent contributor to soil-lead in Santa Ana's environment. This research contributes to environmental-justice advocacy efforts to reframe lead poisoning as a systemic environmental issue and outlines the path forward to community-level remediation strategies.

Socioeconomic Disparities of Low-Cost Air Quality Sensors in California, 2017-2020.

Objectives. To (1) examine the disparity in availability of PurpleAir low-cost air quality sensors in California based on neighborhood socioeconomic status (SES) and exposure to fine particulate matter smaller than 2.5 micrometers (PM2.5), (2) investigate the temporal trend of sensor distribution and operation, and (3) identify priority communities for future sensor distribution. Methods. We obtained census tract-level SES variables and PM2.5 concentrations from the CalEnviroScreen4.0 data set. We obtained real-time PurpleAir sensor data (July 2017-September 2020) to examine sensor distribution and operation. We conducted spatial and temporal analyses at the census tract level to investigate neighborhood SES and PM2.5 concentrations in relation to sensor distribution and operation. Results. The spatial coverage and the number of PurpleAir sensors increased significantly in California. Fewer sensors were distributed in census tracts with lower SES, higher PM2.5, and higher proportions of racial/ethnic minority populations. Furthermore, a large proportion of existing sensors were not in operation at a given time, especially in disadvantaged communities. Conclusions. Disadvantaged communities should be given access to low-cost sensors to fill in spatial gaps of air quality monitoring and address environmental justice concerns. Sensor purchasing and deployment must be paired with regular maintenance to ensure their reliable performance. (Am J Public Health. 2022;112(3):434-442. https://doi.org/10.2105/AJPH.2021.306603).

Compound Risk of Air Pollution and Heat Days and the Influence of Wildfire by SES across California, 2018-2020: Implications for Environmental Justice in the Context of Climate Change.

Major wildfires and heatwaves have begun to increase in frequency throughout much of the United States, particularly in western states such as California, causing increased risk to public health. Air pollution is exacerbated by both wildfires and warmer temperatures, thus adding to such risk. With climate change and the continued increase in global average temperatures, the frequency of major wildfires, heat days, and unhealthy air pollution episodes is projected to increase, resulting in the potential for compounding risks. Risks will likely vary by region and may disproportionately impact low-income communities and communities of color. In this study, we processed daily particulate matter (PM) data from over 18,000 low-cost PurpleAir sensors, along with gridMET daily maximum temperature data and government-compiled wildfire perimeter data from 2018-2020 in order to examine the occurrence of compound risk (CR) days (characterized by high temperature and high PM2.5) at the census tract level in California, and to understand how such days have been impacted by the occurrence of wildfires. Using American Community Survey data, we also examined the extent to which CR days were correlated with household income, race/ethnicity, education, and other socioeconomic factors at the census tract level. Results showed census tracts with a higher frequency of CR days to have statistically higher rates of poverty and unemployment, along with high proportions of child residents and households without computers. The frequency of CR days and elevated daily PM2.5 concentrations appeared to be strongly related to the occurrence of nearby wildfires, with over 20% of days with sensor-measured average PM2.5 > 35 µg/m3 showing a wildfire within a 100 km radius and over two-thirds of estimated CR days falling on such days with a nearby wildfire. Findings from this study are important to policymakers and government agencies who preside over the allocation of state resources as well as organizations seeking to empower residents and establish climate resilient communities.

Impact of 4th of July Fireworks on Spatiotemporal PM2.5 Concentrations in California Based on the PurpleAir Sensor Network: Implications for Policy and Environmental Justice.

Fireworks are often used in celebration, causing short term, extremely high particulate matter air pollution. In recent years, the rapid development and expansion of low-cost air quality sensors by companies such as PurpleAir has enabled an understanding of air pollution at a much higher spatiotemporal resolution compared to traditional monitoring networks. In this study, real-time PM2.5 measurements from 751 PurpleAir sensors operating from June to July in 2019 and 2020 were used to examine the impact of 4th of July fireworks on hourly and daily PM2.5 concentrations at the census tract and county levels in California. American Community Survey (ACS) and CalEnviroScreen 3.0 data were used to identify correlations between PM2.5 measurements and socioeconomic status (SES). A two-step method was implemented to assure the quality of raw PM2.5 sensor data and sensor calibration against co-located reference instruments. The results showed that over 67% and 81% of counties experienced immediate impacts related to fireworks in 2019 and 2020, respectively. Relative to 2019, the peak PM2.5 concentrations on July 4th and 5th 2020 were, on average, over 50% higher in California, likely due to the COVID-19-related increase in the use of household-level fireworks. This increase was most pronounced in southern counties, which tend to have less strict firework-related regulations and a greater use of illegal fireworks. Los Angeles County experienced the highest July 4th daily PM2.5 levels both in 2019 (29.9 µg·m-3) and 2020 (42.6 µg·m-3). Spatial hot spot analyses generally showed these southern counties (e.g., Los Angeles County) to be regional air pollution hotspots, whereas the opposite pattern was seen in the north (e.g., San Francisco). The results also showed PM2.5 peaks that were over two-times higher among communities with lower SES, higher minority group populations, and higher asthma rates. Our findings highlight the important role that policy and enforcement can play in reducing firework-related air pollution and protecting public health, as exemplified by southern California, where policy was more relaxed and air pollution was higher (especially in 2020 when the 4th of July coincided with the COVID-19-lockdown period), and in disadvantaged communities where disparities were greatest.

Risk assessment of soil heavy metal contamination at the census tract level in the city of Santa Ana, CA: implications for health and environmental justice.

(1) Background: exposure to heavy metals is associated with adverse health effects and disproportionately impacts low-income communities and communities of color. We carried out a community-based participatory research study to examine the distribution of heavy metal concentrations in the soil and social vulnerabilities to soil heavy metal exposures across Census tracts in Santa Ana, CA. (2) Methods: soil samples (n = 1528) of eight heavy metals including lead (Pb), arsenic (As), manganese (Mn), chromium (Cr), nickel (Ni), copper (Cu), cadmium (Cd), and zinc (Zn) were collected in 2018 across Santa Ana, CA, at a high spatial resolution and analyzed using XRF analysis. Metal concentrations were mapped out and American Community Survey data was utilized to assess metals throughout Census tracts in terms of social and economic variables. Risk assessment was conducted to evaluate carcinogenic and non-carcinogenic risk. (3) Results: concentrations of soil metals varied according to landuse type and socioeconomic factors. Census tracts where the median household income was under $50 000 had 390%, 92.9%, 56.6%, and 54.3% higher Pb, Zn, Cd, and As concentrations compared to high-income counterparts. All Census tracts in Santa Ana showed hazard index >1, implying the potential for non-carcinogenic health effects, and nearly all Census tracts showed a cancer risk above 10-4, implying a greater than acceptable risk. Risk was predominantly driven by childhood exposure. (4) Conclusions: findings inform initiatives related to environmental justice and highlight subpopulations at elevated risk of heavy metal exposure, in turn underscoring the need for community-driven recommendations for policies and other actions to remediate soil contamination and protect the health of residents.

Disproportionate Impacts of Wildfires among Elderly and Low-Income Communities in California from 2000-2020.

Wildfires can be detrimental to urban and rural communities, causing impacts in the form of psychological stress, direct physical injury, and smoke-related morbidity and mortality. This study examined the area burned by wildfires over the entire state of California from the years 2000 to 2020 in order to quantify and identify whether burned area and fire frequency differed across Census tracts according to socioeconomic indicators over time. Wildfire data were obtained from the California Fire and Resource Assessment Program (FRAP) and National Interagency Fire Center (NIFC), while demographic data were obtained from the American Community Survey. Results showed a doubling in the number of Census tracts that experienced major wildfires and a near doubling in the number of people residing in wildfire-impacted Census tracts, mostly due to an over 23,000 acre/year increase in the area burned by wildfires over the last two decades. Census tracts with a higher fire frequency and burned area had lower proportions of minority groups on average. However, when considering Native American populations, a greater proportion resided in highly impacted Census tracts. Such Census tracts also had higher proportions of older residents. In general, high-impact Census tracts tended to have higher proportions of low-income residents and lower proportions of high-income residents, as well as lower median household incomes and home values. These findings are important to policymakers and state agencies as it relates to environmental justice and the allocation of resources before, during, and after wildfires in the state of California.

Social and spatial distribution of soil lead concentrations in the City of Santa Ana, California: Implications for health inequities.

BACKGROUND: Lead (Pb) exposure is a problem that disproportionately impacts low-income communities and communities of color. We applied a community-based participatory research approach to assess the distribution of soil Pb concentrations and related social vulnerabilities across Census tracts in Santa Ana, CA. METHODS: Soil Pb samples (n = 1528) were collected by the ¡Plo-NO! Santa Ana! Lead-Free Santa Ana! partnership in 2018 across Santa Ana, CA, at a high spatial resolution and measured using XRF analysis. Pb concentrations were mapped and spatial interpolation was conducted to generate a continuous smoothed map of soil Pb concentrations across the city. American Community Survey data was used to examine Pb across Census tracts based on social and economic factors, and to allow for the development of a Cumulative Risk Index to identify areas at high risk of health impacts. RESULTS: Soil Pb concentrations varied by landuse type and socioeconomic factors. Census tracts with a median household income below $50,000 had over five times higher soil Pb concentrations than high-income Census tracts. Soil samples collected in tertiles with the highest percent children, residents without health insurance, renter-occupied housing units, and lowest percent college educated residents had 90.0%, 96.1%, 75.2%, and 87.0% higher Pb concentrations on average, respectively, compared to their counterparts. Overall, 52.7% of residential samples had Pb concentrations in excess of the 80 ppm California EPA recommendation, and 11 Census tracts were characterized as high risk according to our Cumulative Risk Index. DISCUSSION: This study underscores the need for precautionary measures relating to disturbances of the soil, particularly for areas where children play outside, given children's higher absorption of lead. It also informs environmental justice initiatives and identifies vulnerable subpopulations at greater risk of Pb exposure, thus warranting community-driven recommendations for policies and initiatives to remediate soil Pb and protect public health and health equity.

Association Between Outdoor Air Pollution and Risk of Malignant and Benign Brain Tumors: The Multiethnic Cohort Study.

BACKGROUND: There are increasing concerns about the potential impact of air pollution on chronic brain inflammation and microglia cell activation, but evidence of its carcinogenic effects is limited. METHODS: We used kriging interpolation and land use regression models to estimate long-term air pollutant exposures of oxides of nitrogen (NOx, NO2), kriging interpolation for ozone (O3), carbon monoxide, and particulate matter (PM2.5, PM10), and nearest monitoring station measurements for benzene for 103 308 men and women from the Multiethnic Cohort, residing largely in Los Angeles County from recruitment (1993-1996) through 2013. We used Cox proportional hazards models to examine the associations between time-varying pollutants and risk of malignant brain cancer (94 men, 116 women) and meningioma (130 men, 425 women) with adjustment for sex, race and ethnicity, neighborhood socioeconomic status, smoking, occupation, and other covariates. Stratified analyses were conducted by sex and race and ethnicity. RESULTS: Brain cancer risk in men increased in association with exposure to benzene (hazard ratio [HR] = 3.52, 95% confidence interval [CI] = 1.55 to 7.55) and PM10 (HR = 1.80, 95% CI = 1.00 to 3.23). Stronger associations with PM10 (HR = 3.02, 95% CI = 1.26 to 7.23), O3 (HR = 2.93, 95% CI = 1.09 to 7.88), and benzene (HR = 4.06, 95% CI = 1.17 to 18.2) were observed among Latino men. Air pollution was unrelated to risk of meningioma except that O3 exposure was associated with risk in men (HR = 1.77, 95% CI = 1.02 to 3.06). Brain cancer risk in women was unrelated to air pollution exposures. CONCLUSIONS: Confirmation of these sex differences in air pollution-brain cancer associations and the stronger findings in Latino men in additional diverse populations is warranted.

Use of Twitter data to improve Zika virus surveillance in the United States during the 2016 epidemic.

BACKGROUND: Zika virus (ZIKV) is an emerging mosquito-borne arbovirus that can produce serious public health consequences. In 2016, ZIKV caused an epidemic in many countries around the world, including the United States. ZIKV surveillance and vector control is essential to combating future epidemics. However, challenges relating to the timely publication of case reports significantly limit the effectiveness of current surveillance methods. In many countries with poor infrastructure, established systems for case reporting often do not exist. Previous studies investigating the H1N1 pandemic, general influenza and the recent Ebola outbreak have demonstrated that time- and geo-tagged Twitter data, which is immediately available, can be utilized to overcome these limitations. METHODS: In this study, we employed a recently developed system called Cloudberry to filter a random sample of Twitter data to investigate the feasibility of using such data for ZIKV epidemic tracking on a national and state (Florida) level. Two auto-regressive models were calibrated using weekly ZIKV case counts and zika tweets in order to estimate weekly ZIKV cases 1 week in advance. RESULTS: While models tended to over-predict at low case counts and under-predict at extreme high counts, a comparison of predicted versus observed weekly ZIKV case counts following model calibration demonstrated overall reasonable predictive accuracy, with an R2 of 0.74 for the Florida model and 0.70 for the U.S. MODEL: Time-series analysis of predicted and observed ZIKV cases following internal cross-validation exhibited very similar patterns, demonstrating reasonable model performance. Spatially, the distribution of cumulative ZIKV case counts (local- & travel-related) and zika tweets across all 50 U.S. states showed a high correlation (r = 0.73) after adjusting for population. CONCLUSIONS: This study demonstrates the value of utilizing Twitter data for the purposes of disease surveillance. This is of high value to epidemiologist and public health officials charged with protecting the public during future outbreaks.

Cardiovascular diseases in middle aged and older adults in China: the joint effects and mediation of different types of physical exercise and neighborhood greenness and walkability.

BACKGROUND: Both physical exercise and the built environment are associated with cardiovascular diseases (CVDs). Yet, the influence of the multiple dimensions of the built environment and different types of physical exercise on CVDs is not well understood. Further, little is known about the joint effects of physical exercise and the built environment, nor whether one mediates the effect of the other on the risk of CVDs. We aim to investigate the risk of CVDs on middle aged and older Chinese adult populations by analyzing the independent effects, as well as potential interactions and mediation effects of different types of physical exercise and two dimensions of the built environment; namely, greenness and walkability. METHODS: Data were collected from a community-based cross-sectional study (n = 1944). The study participants, aged 40 years or older, came from 32 communities across urban, suburban, and rural areas in Longzihu district of Bengbu, a typical second-tier city in eastern China. Physical exercise data were obtained from the International Physical Activity Questionnaire (IPAQ) question survey. We used a satellite-based Normalized Difference Vegetation Index (NDVI) score to assess greenness exposure. We used both the Walk Score index and the Neighborhood Environment Walkability Scale (NEWS) to assess walkability. Multilevel logistic regression, also known as mixed-effects logistic regression, was used to estimate the associations between physical exercise and the built environment (greenness and walkability) on CVD outcomes while accounting for within-community and within-subdistrict correlations. We followed Baron and Kenny's framework and used bootstrapping to quantify the mediation of physical exercise between built environment and CVD outcomes. Stratified analysis was conducted by age (middle aged and older adults) and gender. RESULTS: Compared to the reference group with little to low physical activities, we found a significantly reduced risk of hypertension (about 20-45% reduction) and coronary heart disease (about 35-55% reduction) among those with moderate to high activities in walking/square dancing or morning exercising/Tai Chi, and a significantly reduced risk of stroke (about 25% reduction) among those with moderate to high activities in walking/square dancing. Compared to the reference group with low NDVI-based greenness exposure, we found a significant reduction in risk of hypertension (about 55-85% reduction), coronary heart disease (about 75% reduction) and stroke (about 45% reduction) among those with moderate to high levels of exposure. Compared to the reference groups with low walkability, we observed about 30-60% lower risk of hypertension and coronary heart disease associated with moderate to high levels of Walk score, and about 20-30% lower risk of hypertension and stroke associated with moderate to high levels of NEWS-based walkability. We found no interactions between physical exercise and the built environment. The associations of greenness and walkability with CVDs were partially explained by physical exercise (up to 55% of the total effect). CONCLUSIONS: Both physical exercise and built environment factors were associated with the risk of CVDs. Our observed association between CVDs and neighborhood greenness exposure and walkability was explained, in part, by physical exercises. Such a role, if confirmed in future studies, could have important implications for policies and programs aimed at increasing green spaces and improving walkability in both urban and rural settings as strategies to promote physical exercise in middle aged and older population.

Source Characterization and Exposure Modeling of Gas-Phase Polycyclic Aromatic Hydrocarbon (PAH) Concentrations in Southern California.

Airborne exposures to polycyclic aromatic hydrocarbons (PAHs) are associated with adverse health outcomes. Because personal air measurements of PAHs are labor intensive and costly, spatial PAH exposure models are useful for epidemiological studies. However, few studies provide adequate spatial coverage to reflect intra-urban variability of ambient PAHs. In this study, we collected 39-40 weekly gas-phase PAH samples in southern California twice in summer and twice in winter, 2009, in order to characterize PAH source contributions and develop spatial models that can estimate gas-phase PAH concentrations at a high resolution. A spatial mixed regression model was constructed, including such variables as roadway, traffic, land-use, vegetation index, commercial cooking facilities, meteorology, and population density. Cross validation of the model resulted in an R2 of 0.66 for summer and 0.77 for winter. Results showed higher total PAH concentrations in winter. Pyrogenic sources, such as fossil fuels and diesel exhaust, were the most dominant contributors to total PAHs. PAH sources varied by season, with a higher fossil fuel and wood burning contribution in winter. Spatial autocorrelation accounted for a substantial amount of the variance in total PAH concentrations for both winter (56%) and summer (19%). In summer, other key variables explaining the variance included meteorological factors (9%), population density (15%), and roadway length (21%). In winter, the variance was also explained by traffic density (16%). In this study, source characterization confirmed the dominance of traffic and other fossil fuel sources to total measured gas-phase PAH concentrations while a spatial exposure model identified key predictors of PAH concentrations. Gas-phase PAH source characterization and exposure estimation is of high utility to epidemiologist and policy makers interested in understanding the health impacts of gas-phase PAHs and strategies to reduce emissions.

A novel calibration approach using satellite and visibility observations to estimate fine particulate matter exposures in Southwest Asia and Afghanistan.

In order to study effects of ambient particulate matter (PM) it was previously necessary to have access to a comprehensive air monitoring network. However, there are locations in the world where PM levels are above generally accepted exposure standards but lack a monitoring infrastructure. This is true in Iraq and other locations in Southwest Asia and Afghanistan where U.S. and other coalition troops were deployed beginning in 2001. Since aerosol optical depth (AOD), determined by satellite, and visibility are both highly related to atmospheric PM2.5 (particulate matter with an aerodynamic diameter ≤2.5 µm) concentrations, we employed a novel approach that took advantage of historic airport visibility measurements to calibrate the AOD-visibility relationship and determine visibility spatially and temporally (2006-2007) over an approximately 17,000 km2 region of Iraq. We obtained daily visibility predictions that were highly associated with satellite-based 1x1 km AOD daily observations (R2=0.87). Based on a previously derived calibration between PM2.5 and visibility, we were able to predict spatially and temporally resolved PM2.5 concentrations. Variability of PM2.5 among sites was high, with daily concentrations differing by as much as ~30 µg/m3. This study demonstrates the feasibility of characterizing historic PM2.5 exposures in Iraq and other locations in Southwest Asia and Afghanistan with similar climate characteristics. This is of utility for epidemiologists seeking to assess the potential health effects related to PM2.5 exposures among previously deployed military personnel and of the population of the region. IMPLICATIONS: This study demonstrates the ability to utilize aerosol optical depth to successfully estimate visibility spatially and temporally in Southwest Asia and Afghanistan. This enables for the estimation of spatially resolved PM2.5 concentrations in the region. The ability to caracterize PM2.5 concentrations in Southwest Asia and Afghanistan is highly important for epidemiologists investigating the relationship between chronic exposure to PM2.5 and respiratory diseases among military personnel deployed to the region. This information will better position policy makers to draft meaningful legislation relating to military health.

Use of visual range measurements to predict fine particulate matter exposures in Southwest Asia and Afghanistan.

Military personnel deployed to Southwest Asia and Afghanistan were exposed to high levels of ambient particulate matter (PM). However, quantitative ambient exposure data for conducting health studies are limited due to a lack of PM monitoring stations. Since visual range (VR) is proportional to particle light extinction, VR can serve as a surrogate for PM2.5 (particulate matter with an aerodynamic diameter ≤2.5 µm) concentrations. We used data on VR, relative humidity (RH), and PM2.5 ground measurements collected in Kuwait from years 2004-2005 to establish the relationship between PM2.5 and VR. Model validation obtained by regressing trimester average PM2.5 predictions against PM2.5 measurements in Kuwait produced an r2 value of 0.84. Cross validation of urban and rural sites in Kuwait also revealed good model fit. We applied this relationship to location-specific visibility data at 104 regional sites between years 2000-2012 to estimate monthly average PM2.5 concentrations. Monthly averages at sites in Iraq, Afghanistan, United Arab Emirates, Kuwait, Djibouti, and Qatar ranged from 10 to 365 µg/m3 during this period, while site averages ranged from 22 to 80 µg/m3, indicating considerable spatial and temporal heterogeneity in ambient PM2.5 across these regions. These data support the use of historical visibility data to estimate location-specific PM2.5 concentrations for application in epidemiological studies. IMPLICATIONS: This study demonstrates the ability to use airport visibility to estimate PM2.5 concentrations in Southwest Asian and Afghanistan. This supports the use of historical and ongoing visibility data to estimate PM2.5 exposure in this region of the world, where PM exposure information is otherwise scarce. This is of high utility to epidemiologists investigating the relationship between chronic exposure to PM2.5 and respiratory diseases among deployed military personnel stationed at various military bases throughout the region. Such information will enable the drafting of improved policies relating to military health.

Composition and sources of fine and coarse particles collected during 2002-2010 in Boston, MA.

UNLABELLED: Identifying the sources, composition, and temporal variability of fine (PM2.5) and coarse (PM2.5-10) particles is a crucial component in understanding particulate matter (PM) toxicity and establishing proper PM regulations. In this study, a Harvard Impactor was used to collect daily integrated fine and coarse particle samples every third day for 9 years at a single site in Boston, MA. In total, 1,960 filters were analyzed for elements, black carbon (BC), and total PM mass. Positive Matrix Factorization (PMF) was used to identify source types and quantify their contributions to ambient PM2.5 and PM2.5-10. BC and 17 elements were identified as the main constituents in our samples. Results showed that BC, S, and Pb were associated exclusively with the fine particle mode, while 84% of V and 79% of Ni were associated with this mode. Elements mostly found in the coarse mode, over 80%, included Ca, Mn (road dust), and Cl (sea salt). PMF identified six source types for PM2.5 and three source types for PM2.5-10. Source types for PM2.5 included regional pollution, motor vehicles, sea salt, crustal/road dust, oil combustion, and wood burning. Regional pollution contributed the most, accounting for 48% of total PM2.5 mass, followed by motor vehicles (21%) and wood burning (19%). Source types for PM2.5-10 included crustal/road dust (62%), motor vehicles (22%), and sea salt (16%). A linear decrease in PM concentrations with time was observed for both fine (-5.2%/yr) and coarse (-3.6%/yr) particles. The fine-mode trend was mostly related to oil combustion and regional pollution contributions. Average PM2.5 concentrations peaked in summer (10.4 µg/m3), while PM2.5-10 concentrations were lower and demonstrated little seasonal variability. The findings of this study show that PM2.5 is decreasing more sharply than PM2.5-10 over time. This suggests the increasing importance of PM2.5-10 and traffic-related sources for PM exposure and future policies. IMPLICATIONS: Although many studies have examined fine and coarse particle composition and sources, few studies have used concurrent measurements of these two fractions. Our analysis suggests that fine and coarse particles exhibit distinct compositions and sources. With better knowledge of the compositional and source differences between these two PM fractions, better decisions can be made about PM regulations. Further, such information is valuable in enabling epidemiologists to understand the ensuing health implications of PM exposure.