Community- Versus Health Care Organization-Based Approaches to Expanding At-Home COVID-19 Testing in Black and Latino Communities, New Jersey, 2021.

At-home COVID-19 testing offers convenience and safety advantages. We evaluated at-home testing in Black and Latino communities through an intervention comparing community-based organization (CBO) and health care organization (HCO) outreach. From May through December 2021, 1100 participants were recruited, 94% through CBOs. The odds of COVID-19 test requests and completions were significantly higher in the HCO arm. The results showed disparities in test requests and completions related to age, race, language, insurance, comorbidities, and pandemic-related challenges. Despite the popularity of at-home testing, barriers exist in underresourced communities. (Am J Public Health. 2022;112(S9):S918-S922. https://doi.org/10.2105/AJPH.2022.306989).

Flexible Bayesian Ensemble Machine Learning Framework for Predicting Local Ozone Concentrations.

3D-grid-based chemical transport models, such as the Community Multiscale Air Quality (CMAQ) modeling system, have been widely used for predicting concentrations of ambient air pollutants. However, typical horizontal resolutions of nationwide CMAQ simulations (12 × 12 km2) cannot capture local-scale gradients for accurately assessing human exposures and environmental justice disparities. In this study, a Bayesian ensemble machine learning (BEML) framework, which integrates 13 learning algorithms, was developed for downscaling CMAQ estimates of ozone daily maximum 8 h averages to the census tract level, across the contiguous US, and was demonstrated for 2011. Three-stage hyperparameter tuning and targeted validations were designed to ensure the ensemble model's ability to interpolate, extrapolate, and capture concentration peaks. The Shapley value metric from coalitional game theory was applied to interpret the drivers of subgrid gradients. The flexibility (transferability) of the 2011-trained BEML model was further tested by evaluating its ability to estimate fine-scale concentrations for other years (2012-2017) without retraining. To demonstrate the feasibility of using the BEML approach to strictly "data-limited" situations, the model was applied to downscale CMAQ outputs for a future-year scenario-based simulation that considers effects of variations in meteorology associated with climate change.

Allergenic pollen season variations in the past two decades under changing climate in the United States.

Many diseases are linked with climate trends and variations. In particular, climate change is expected to alter the spatiotemporal dynamics of allergenic airborne pollen and potentially increase occurrence of allergic airway disease. Understanding the spatiotemporal patterns of changes in pollen season timing and levels is thus important in assessing climate impacts on aerobiology and allergy caused by allergenic airborne pollen. Here, we describe the spatiotemporal patterns of changes in the seasonal timing and levels of allergenic airborne pollen for multiple taxa in different climate regions at a continental scale. The allergenic pollen seasons of representative trees, weeds and grass during the past decade (2001-2010) across the contiguous United States have been observed to start 3.0 [95% Confidence Interval (CI), 1.1-4.9] days earlier on average than in the 1990s (1994-2000). The average peak value and annual total of daily counted airborne pollen have increased by 42.4% (95% CI, 21.9-62.9%) and 46.0% (95% CI, 21.5-70.5%), respectively. Changes of pollen season timing and airborne levels depend on latitude, and are associated with changes of growing degree days, frost free days, and precipitation. These changes are likely due to recent climate change and particularly the enhanced warming and precipitation at higher latitudes in the contiguous United States.

Predicting Onset and Duration of Airborne Allergenic Pollen Season in the United States.

Allergenic pollen is one of the main triggers of Allergic Airway Disease (AAD) affecting 5% to 30% of the population in industrialized countries. A modeling framework has been developed using correlation and collinearity analyses, simulated annealing, and stepwise regression based on nationwide observations of airborne pollen counts and climatic factors to predict the onsets and durations of allergenic pollen seasons of representative trees, weeds and grass in the contiguous United States. Main factors considered are monthly, seasonal and annual mean temperatures and accumulative precipitations, latitude, elevation, Growing Degree Day (GDD), Frost Free Day (FFD), Start Date (SD) and Season Length (SL) in the previous year. The estimated mean SD and SL for birch (Betula), oak (Quercus), ragweed (Ambrosia), mugwort (Artemisia) and grass (Poaceae) pollen season in 1994-2010 are mostly within 0 to 6 days of the corresponding observations for the majority of the National Allergy Bureau (NAB) monitoring stations across the contiguous US. The simulated spatially resolved maps for onset and duration of allergenic pollen season in the contiguous US are consistent with the long term observations.

A tiered framework for risk-relevant characterization and ranking of chemical exposures: applications to the National Children's Study (NCS).

A challenge for large-scale environmental health investigations such as the National Children's Study (NCS), is characterizing exposures to multiple, co-occurring chemical agents with varying spatiotemporal concentrations and consequences modulated by biochemical, physiological, behavioral, socioeconomic, and environmental factors. Such investigations can benefit from systematic retrieval, analysis, and integration of diverse extant information on both contaminant patterns and exposure-relevant factors. This requires development, evaluation, and deployment of informatics methods that support flexible access and analysis of multiattribute data across multiple spatiotemporal scales. A new "Tiered Exposure Ranking" (TiER) framework, developed to support various aspects of risk-relevant exposure characterization, is described here, with examples demonstrating its application to the NCS. TiER utilizes advances in informatics computational methods, extant database content and availability, and integrative environmental/exposure/biological modeling to support both "discovery-driven" and "hypothesis-driven" analyses. "Tier 1" applications focus on "exposomic" pattern recognition for extracting information from multidimensional data sets, whereas second and higher tier applications utilize mechanistic models to develop risk-relevant exposure metrics for populations and individuals. In this article, "tier 1" applications of TiER explore identification of potentially causative associations among risk factors, for prioritizing further studies, by considering publicly available demographic/socioeconomic, behavioral, and environmental data in relation to two health endpoints (preterm birth and low birth weight). A "tier 2" application develops estimates of pollutant mixture inhalation exposure indices for NCS counties, formulated to support risk characterization for these endpoints. Applications of TiER demonstrate the feasibility of developing risk-relevant exposure characterizations for pollutants using extant environmental and demographic/socioeconomic data.

Climate change effect on Betula (birch) and Quercus (oak) pollen seasons in the United States.

Climatic change is expected to affect the spatiotemporal patterns of airborne allergenic pollen, which has been found to act synergistically with common air pollutants, such as ozone, to cause allergic airway disease (AAD). Observed airborne pollen data from six stations from 1994 to 2011 at Fargo (North Dakota), College Station (Texas), Omaha (Nebraska), Pleasanton (California), Cherry Hill and Newark (New Jersey) in the US were studied to examine climate change effects on trends of annual mean and peak value of daily concentrations, annual production, season start, and season length of Betula (birch) and Quercus (oak) pollen. The growing degree hour (GDH) model was used to establish a relationship between start/end dates and differential temperature sums using observed hourly temperatures from surrounding meteorology stations. Optimum GDH models were then combined with meteorological information from the Weather Research and Forecasting (WRF) model, and land use land coverage data from the Biogenic Emissions Land use Database, version 3.1 (BELD3.1), to simulate start dates and season lengths of birch and oak pollen for both past and future years across the contiguous US (CONUS). For most of the studied stations, comparison of mean pollen indices between the periods of 1994-2000 and 2001-2011 showed that birch and oak trees were observed to flower 1-2 weeks earlier; annual mean and peak value of daily pollen concentrations tended to increase by 13.6%-248%. The observed pollen season lengths varied for birch and for oak across the different monitoring stations. Optimum initial date, base temperature, and threshold GDH for start date was found to be 1 March, 8 °C, and 1,879 h, respectively, for birch; 1 March, 5 °C, and 4,760 h, respectively, for oak. Simulation results indicated that responses of birch and oak pollen seasons to climate change are expected to vary for different regions.

A semi-mechanistic integrated toxicokinetic-toxicodynamic (TK/TD) model for arsenic(III) in hepatocytes.

BACKGROUND: A systems engineering approach is presented for describing the kinetics and dynamics that are elicited upon arsenic exposure of human hepatocytes. The mathematical model proposed here tracks the cellular reaction network of inorganic and organic arsenic compounds present in the hepatocyte and analyzes the production of toxicologically potent by-products and the signaling they induce in hepatocytes. METHODS AND RESULTS: The present modeling effort integrates for the first time a cellular-level semi-mechanistic toxicokinetic (TK) model of arsenic in human hepatocytes with a cellular-level toxicodynamic (TD) model describing the arsenic-induced reactive oxygen species (ROS) burst, the antioxidant response, and the oxidative DNA damage repair process. The antioxidant response mechanism is described based on the Keap1-independent Nuclear Factor-erythroid 2-related factor 2 (Nrf2) signaling cascade and accounts for the upregulation of detoxifying enzymes. The ROS-induced DNA damage is simulated by coupling the TK/TD formulation with a model describing the multistep pathway of oxidative DNA repair. The predictions of the model are assessed against experimental data of arsenite-induced genotoxic damage to human hepatocytes; thereby capturing in silico the mode of the experimental dose-response curve. CONCLUSIONS: The integrated cellular-level TK/TD model presented here provides significant insight into the underlying regulatory mechanism of Nrf2-regulated antioxidant response due to arsenic exposure. While computational simulations are in a fair good agreement with relevant experimental data, further analysis of the system unravels the role of a dynamic interplay among the feedback loops of the system in controlling the ROS upregulation and DNA damage response. This TK/TD framework that uses arsenic as an example can be further extended to other toxic or pharmaceutical agents.

Modeling flight attendants' exposures to pesticide in disinsected aircraft cabins.

Aircraft cabin disinsection is required by some countries to kill insects that may pose risks to public health and native ecological systems. A probabilistic model has been developed by considering the microenvironmental dynamics of the pesticide in conjunction with the activity patterns of flight attendants, to assess their exposures and risks to pesticide in disinsected aircraft cabins under three scenarios of pesticide application. Main processes considered in the model are microenvironmental transport and deposition, volatilization, and transfer of pesticide when passengers and flight attendants come in contact with the cabin surfaces. The simulated pesticide airborne mass concentration and surface mass loadings captured measured ranges reported in the literature. The medians (means ± standard devitions) of daily total exposure intakes were 0.24 (3.8 ± 10.0), 1.4 (4.2 ± 5.7), and 0.15 (2.1 ± 3.2) µg day(-1) kg(-1) of body weight for scenarios of residual application, preflight, and top-of-descent spraying, respectively. Exposure estimates were sensitive to parameters corresponding to pesticide deposition, body surface area and weight, surface-to-body transfer efficiencies, and efficiency of adherence to skin. Preflight spray posed 2.0 and 3.1 times higher pesticide exposure risk levels for flight attendants in disinsected aircraft cabins than top-of-descent spray and residual application, respectively.

Bayesian Analysis of Climate Change Effects on Observed and Projected Airborne Levels of Birch Pollen.

A Bayesian framework is presented for modeling Effects of climate change on pollen indices such as annual birch pollen count, maximum daily birch pollen count, start date of birch pollen season and the date of maximum daily birch pollen count. Annual mean CO2 concentration, mean spring temperature and the corresponding pollen index of prior year were found to be statistically significant accounting for Effects of climate change on four pollen indices. Results suggest that annual productions and peak values from 2020 to 2100 under different scenarios will be 1.3-8.0 and 1.1-7.3 times higher respectively than the mean values for 2000, and start and peak dates will occur around two to four weeks earlier. These results have been partly confirmed by the available historical data. As a demonstration, the emission profiles in future years were generated by incorporating the predicted pollen indices into an existing emission model.

Socioexposomics of COVID-19 across New Jersey: a comparison of geostatistical and machine learning approaches.

BACKGROUND: Disparities in adverse COVID-19 health outcomes have been associated with multiple social and environmental stressors. However, research is needed to evaluate the consistency and efficiency of methods for studying these associations at local scales. OBJECTIVE: To assess socioexposomic associations with COVID-19 outcomes across New Jersey and evaluate consistency of findings from multiple modeling approaches. METHODS: We retrieved data for COVID-19 cases and deaths for the 565 municipalities of New Jersey up to the end of the first phase of the pandemic, and calculated mortality rates with and without long-term-care (LTC) facility deaths. We considered 84 spatially heterogeneous environmental, demographic and socioeconomic factors from publicly available databases, including air pollution, proximity to industrial sites/facilities, transportation-related noise, occupation and commuting, neighborhood and housing characteristics, age structure, racial/ethnic composition, poverty, etc. Six geostatistical models (Poisson/Negative-Binomial regression, Poison/Negative-Binomial mixed effect model, Poisson/Negative-Binomial Bersag-York-Mollie spatial model) and two Machine Learning (ML) methods (Random Forest, Extreme Gradient Boosting) were implemented to assess association patterns. The Shapley effects plot was established for explainable ML and change of support validation was introduced to compare performances of different approaches. RESULTS: We found robust positive associations of COVID-19 mortality with historic exposures to NO2, population density, percentage of minority and below high school education, and other social and environmental factors. Exclusion of LTC deaths does not significantly affect correlations for most factors but findings can be substantially influenced by model structures and assumptions. The best performing geostatistical models involved flexible structures representing data variations. ML methods captured association patterns consistent with the best performing geostatistical models, and furthermore detected consistent nonlinear associations not captured by geostatistical models. SIGNIFICANCE: The findings of this work improve the understanding of how social and environmental disparities impacted COVID-19 outcomes across New Jersey.

Connect: Cultivating Academic-Community Partnerships to Address Our Communities' Complex Needs During Public Health Crises.

BACKGROUND: Black and Latino communities have been disproportionately impacted by coronavirus disease 2019 and we sought to understand perceptions and attitudes in four heavily impacted New Jersey counties to develop and evaluate engagement strategies to enhance access to testing. OBJECTIVE: To establish a successful academic/community partnership team during a public health emergency by building upon longstanding relationships and using principles from community engaged research. METHODS: We present a case study illustrating multiple levels of engagement, showing how we successfully aligned expectations, developed a commitment of cooperation, and implemented a research study, with community-based and health care organizations at the center of community engagement and recruitment. LESSONS LEARNED: This paper describes successful approaches to relationship building including information sharing and feedback to foster reciprocity, diverse dissemination strategies to enhance engagement, and intergenerational interaction to ensure sustainability. CONCLUSIONS: This model demonstrates how academic/community partnerships can work together during public health emergencies to develop sustainable relationships.

Modeling past and future spatiotemporal distributions of airborne allergenic pollen across the contiguous United States.

Exposures to airborne allergenic pollen have been increasing under the influence of changing climate. A modeling system incorporating pollen emissions and atmospheric transport and fate processes has been developed and applied to simulate spatiotemporal distributions of two major aeroallergens, oak and ragweed pollens, across the contiguous United States (CONUS) for both historical (year 2004) and future (year 2047) conditions. The transport and fate of pollen presented here is simulated using our adapted version of the Community Multiscale Air Quality (CMAQ) model. Model performance was evaluated using observed pollen counts at monitor stations across the CONUS for 2004. Our analysis shows that there is encouraging consistency between observed seasonal mean concentrations and corresponding simulated seasonal mean concentrations (oak: Pearson = 0.35, ragweed: Pearson = 0.40), and that the model was able to capture the statistical patterns of observed pollen concentration distributions in 2004 for most of the pollen monitoring stations. Simulation of pollen levels for a future year (2047) considered conditions corresponding to the RCP8.5 scenario. Modeling results show substantial regional variability both in the magnitude and directionality of changes in pollen metrics. Ragweed pollen season is estimated to start earlier and last longer for all nine climate regions of the CONUS, with increasing average pollen concentrations in most regions. The timing and magnitude of oak pollen season vary across the nine climate regions, with the largest increases in pollen concentrations expected in the Northeast region.

A mechanistic modeling system for estimating large scale emissions and transport of pollen and co-allergens.

Allergic airway diseases represent a complex health problem which can be exacerbated by the synergistic action of pollen particles and air pollutants such as ozone. Understanding human exposures to aeroallergens requires accurate estimates of the spatial distribution of airborne pollen levels as well as of various air pollutants at different times. However, currently there are no established methods for estimating allergenic pollen emissions and concentrations over large geographic areas such as the United States. A mechanistic modeling system for describing pollen emissions and transport over extensive domains has been developed by adapting components of existing regional scale air quality models and vegetation databases. First, components of the Biogenic Emissions Inventory System (BEIS) were adapted to predict pollen emission patterns. Subsequently, the transport module of the Community Multiscale Air Quality (CMAQ) modeling system was modified to incorporate description of pollen transport. The combined model, CMAQ-pollen, allows for simultaneous prediction of multiple air pollutants and pollen levels in a single model simulation, and uses consistent assumptions related to the transport of multiple chemicals and pollen species. Application case studies for evaluating the combined modeling system included the simulation of birch and ragweed pollen levels for the year 2002, during their corresponding peak pollination periods (April for birch and September for ragweed). The model simulations were driven by previously evaluated meteorological model outputs and emissions inventories for the eastern United States for the simulation period. A semi-quantitative evaluation of CMAQ-pollen was performed using tree and ragweed pollen counts in Newark, NJ for the same time periods. The peak birch pollen concentrations were predicted to occur within two days of the peak measurements, while the temporal patterns closely followed the measured profiles of overall tree pollen. For the case of ragweed pollen, the model was able to capture the patterns observed during September 2002, but did not predict an early peak; this can be associated with a wider species pollination window and inadequate spatial information in current land cover databases. An additional sensitivity simulation was performed to comparatively evaluate the dispersion patterns predicted by CMAQ-pollen with those predicted by the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, which is used extensively in aerobiological studies. The CMAQ estimated concentration plumes matched the equivalent pollen scenario modeled with HYSPLIT. The novel pollen modeling approach presented here allows simultaneous estimation of multiple airborne allergens and other air pollutants, and is being developed as a central component of an integrated population exposure modeling system, the Modeling Environment for Total Risk studies (MENTOR) for multiple, co-occurring contaminants that include aeroallergens and irritants.

Personal and ambient exposures to air toxics in Camden, New Jersey.

Personal exposures and ambient concentrations of air toxics were characterized in a pollution "hot spot" and an urban reference site, both in Camden, New Jersey. The hot spot was the city's Waterfront South neighborhood; the reference site was a neighborhood, about 1 km to the east, around the intersection of Copewood and Davis streets. Using personal exposure measurements, residential ambient air measurements, statistical analyses, and exposure modeling, we examined the impact of local industrial and mobile pollution sources, particularly diesel trucks, on personal exposures and ambient concentrations in the two neighborhoods. Presented in the report are details of our study design, sample and data collection methods, data- and model-analysis approaches, and results and key findings of the study. In summary, 107 participants were recruited from nonsmoking households, including 54 from Waterfront South and 53 from the Copewood-Davis area. Personal air samples were collected for 24 hr and measured for 32 target compounds--11 volatile organic compounds (VOCs*), four aldehydes, 16 polycyclic aromatic hydrocarbons (PAHs), and particulate matter (PM) with an aerodynamic diameter < or = 2.5 microm (PM2.5). Simultaneously with the personal monitoring, ambient concentrations of the target compounds were measured at two fixed monitoring sites, one each in the Waterfront South and Copewood-Davis neighborhoods. To understand the potential impact of local sources of air toxics on personal exposures caused by temporal (weekdays versus weekend days) and seasonal (summer versus winter) variations in source intensities of the air toxics, four measurements were made of each subject, two in summer and two in winter. Within each season, one measurement was made on a weekday and the other on a weekend day. A baseline questionnaire and a time diary with an activity questionnaire were administered to each participant in order to obtain information that could be used to understand personal exposure to specific air toxics measured during each sampling period. Given the number of emission sources of air toxics in Waterfront South, a spatial variation study consisting of three saturation-sampling campaigns was conducted to characterize the spatial distribution of VOCs and aldehydes in the two neighborhoods. Passive samplers were used to collect VOC and aldehyde samples for 24- and 48-hr sampling periods simultaneously at 22 and 16 grid-based sampling sites in Waterfront South and Copewood-Davis, respectively. Results showed that measured ambient concentrations of some target pollutants (mean +/- standard deviation [SD]), such as PM2.5 (31.3 +/- 12.5 microg/m3), toluene (4.24 +/- 5.23 microg/m3), and benzo[a]pyrene (0.36 +/- 0.45 ng/m3), were significantly higher (P < 0.05) in Waterfront South than in Copewood-Davis, where the concentrations of PM2.5, toluene, and benzo[a]pyrene were 25.3 +/- 11.9 microg/m3, 2.46 +/- 3.19 microg/m3, and 0.21 +/- 0.26 ng/m3, respectively. High concentrations of specific air toxics, such as 60 microg/m3 for toluene and 159 microg/m3 for methyl tert-butyl ether (MTBE), were also found in areas close to local stationary sources in Waterfront South during the saturation-sampling campaigns. Greater spatial variation in benzene, toluene, ethylbenzene, and xylenes (known collectively as BTEX) as well as of MTBE was observed in Waterfront South than in Copewood-Davis during days with low wind speed. These observations indicated the significant impact of local emission sources of these pollutants and possibly of other pollutants emitted by individual source types on air pollution in Waterfront South. (Waterfront South is a known hot spot for these pollutants.) There were no significant differences between Waterfront South and Copewood-Davis in mean concentrations of benzene or MTBE, although some stationary sources of the two compounds have been reported in Waterfront South. Further, a good correlation (R > 0.6) was found between benzene and MTBE in both locations. These results suggest that automobile exhausts were the main contributors to benzene and MTBE air pollution in both neighborhoods. Formaldehyde and acetaldehyde concentrations were found to be high in both neighborhoods. Mean (+/- SD) concentrations of formaldehyde were 20.2 +/- 19.5 microg/m3 in Waterfront South and 24.8 +/- 20.8 microg/m3 in Copewood-Davis. A similar trend was observed for the two compounds during the saturation-sampling campaigns. The results indicate that mobile sources (i.e., diesel trucks) had a large impact on formaldehyde and acetaldehyde concentrations in both neighborhoods and that both are aldehyde hot spots. The study also showed that PM2.5, aldehydes, BTEX, and MTBE concentrations in both Waterfront South and Copewood-Davis were higher than ambient background concentrations in New Jersey and than national average concentrations, indicating that both neighborhoods are in fact hot spots for these pollutants. Higher concentrations were observed on weekdays than on weekend days for several compounds, including toluene, ethylbenzene, and xylenes (known collectively as TEX) as well as PAHs and PM2.5. These observations showed the impact on ambient air pollution of higher traffic volumes and more active industrial and commercial operations in the study areas on weekdays. Seasonal variations differed by species. Concentrations of TEX, for example, were found to be higher in winter than in summer in both locations, possibly because of higher emission rates from automobiles and reduced photochemical reactivity in winter. In contrast, concentrations of MTBE were found to be significantly higher in summer than in winter in both locations, possibly because of higher evaporation rates from gasoline in summer. Similarly, concentrations of heavier PAHs, such as benzo[a]pyrene, were found to be higher in winter in both locations, possibly because of higher emission rates from mobile sources, the use of home heating, and the reduced photochemical reactivity of benzo[a]pyrene in winter. In contrast, concentrations of lighter PAHs were found to be higher in summer in both locations, possibly because of volatilization of these compounds from various surfaces in summer. In addition, higher concentrations of formaldehyde were observed in summer than in winter, possibly because of significant contributions from photochemical reactions to formaldehyde air pollution in summer. Personal concentrations of toluene (25.4 +/- 13.5 microg/m3) and acrolein (1.78 +/- 3.7 microg/m3) in Waterfront South were found to be higher than those in the Copewood-Davis neighborhood (13.1 +/- 15.3 microg/m3 for toluene and 1.27 +/- 2.36 microg/m3 for acrolein). However, personal concentrations for most of the other compounds measured in Waterfront South were found to be similar to or lower than those than in Copewood-Davis. (For example, mean +/- SD concentrations were 4.58 +/- 17.3 microg/m3 for benzene, 4.06 +/- 5.32 microg/m3 for MTBE, 16.8 +/- 15.5 microg/m3 for formaldehyde, and 0.40 +/- 0.94 ng/m3 for benzo[a]pyrene in Waterfront South and 9.19 +/- 34.0 microg/m3 for benzene, 6.22 +/- 19.0 microg/m3 for MTBE, 16.0 +/- 16.7 microg/m3 for formaldehyde, and 0.42 +/- 1.08 ng/m3 for benzo[a]pyrene in Copewood-Davis.) This was probably because many of the target compounds had both outdoor and indoor sources. The higher personal concentrations of these compounds in Copewood-Davis might have resulted in part from higher exposure to environmental tobacco smoke (ETS) of subjects from Copewood-Davis. The Spearman correlation coefficient (R) was found to be high for pollutants with significant outdoor sources. The R's for MTBE and carbon tetrachloride, for example, were > 0.65 in both Waterfront South and Copewood-Davis. The R's were moderate or low (0.3-0.6) for compounds with both outdoor and indoor sources, such as BTEX and formaldehyde. A weaker association (R < 0.5) was found for compounds with significant indoor sources, such as BTEX, formaldehyde, PAHs, and PM2.5. The correlations between personal and ambient concentrations of MTBE and BTEX were found to be stronger in Waterfront South than in Copewood-Davis, reflecting the significant impact of local air pollution sources on personal exposure to these pollutants in Waterfront South. Emission-based ambient concentrations of benzene, toluene, and formaldehyde and contributions of ambient exposure to personal concentrations of these three compounds were modeled using atmospheric dispersion modeling and Individual Based Exposure Modeling (IBEM) software, respectively, which were coupled for analysis in the Modeling Environment for Total Risk (MENTOR) system. The compounds were associated with the three types of dominant sources in the two neighborhoods: industrial sources (toluene), exhaust from gasoline-powered motor vehicles (benzene), and exhaust from diesel-powered motor vehicles (formaldehyde). Subsequently, both the calculated and measured ambient concentrations of each of the three compounds were separately combined with the time diaries and activity questionnaires completed by the subjects as inputs to IBEM-MENTOR for estimating personal exposures from ambient sources. Modeled ambient concentrations of benzene and toluene were generally in agreement with the measured ambient concentrations within a factor of two, but the values were underestimated at the high-end percentiles. The major local (neighborhood) contributors to ambient benzene concentrations were from mobile sources in the study areas; both mobile and stationary (point and area) sources contributed to the ambient toluene concentrations. This finding can be used as guidance for developing better emission inventories to characterize, through modeling, the ambient concentrations of air toxics in the study areas. (ABSTRACT TRUNCATED)

Impact of biogenic emission uncertainties on the simulated response of ozone and fine particulate matter to anthropogenic emission reductions.

The role of emissions of volatile organic compounds and nitric oxide from biogenic sources is becoming increasingly important in regulatory air quality modeling as levels of anthropogenic emissions continue to decrease and stricter health-based air quality standards are being adopted. However, considerable uncertainties still exist in the current estimation methodologies for biogenic emissions. The impact of these uncertainties on ozone and fine particulate matter (PM2.5) levels for the eastern United States was studied, focusing on biogenic emissions estimates from two commonly used biogenic emission models, the Model of Emissions of Gases and Aerosols from Nature (MEGAN) and the Biogenic Emissions Inventory System (BEIS). Photochemical grid modeling simulations were performed for two scenarios: one reflecting present day conditions and the other reflecting a hypothetical future year with reductions in emissions of anthropogenic oxides of nitrogen (NOx). For ozone, the use of MEGAN emissions resulted in a higher ozone response to hypothetical anthropogenic NOx emission reductions compared with BEIS. Applying the current U.S. Environmental Protection Agency guidance on regulatory air quality modeling in conjunction with typical maximum ozone concentrations, the differences in estimated future year ozone design values (DVF) stemming from differences in biogenic emissions estimates were on the order of 4 parts per billion (ppb), corresponding to approximately 5% of the daily maximum 8-hr ozone National Ambient Air Quality Standard (NAAQS) of 75 ppb. For PM2.5, the differences were 0.1-0.25 microg/m3 in the summer total organic mass component of DVFs, corresponding to approximately 1-2% of the value of the annual PM2.5 NAAQS of 15 microg/m3. Spatial variations in the ozone and PM2.5 differences also reveal that the impacts of different biogenic emission estimates on ozone and PM2.5 levels are dependent on ambient levels of anthropogenic emissions.

Modeling Effects of Spatial Heterogeneities and Layered Exposure Interventions on the Spread of COVID-19 across New Jersey.

COVID-19 created an unprecedented global public health crisis during 2020-2021. The severity of the fast-spreading infection, combined with uncertainties regarding the physical and biological processes affecting transmission of SARS-CoV-2, posed enormous challenges to healthcare systems. Pandemic dynamics exhibited complex spatial heterogeneities across multiple scales, as local demographic, socioeconomic, behavioral and environmental factors were modulating population exposures and susceptibilities. Before effective pharmacological interventions became available, controlling exposures to SARS-CoV-2 was the only public health option for mitigating the disease; therefore, models quantifying the impacts of heterogeneities and alternative exposure interventions on COVID-19 outcomes became essential tools informing policy development. This study used a stochastic SEIR framework, modeling each of the 21 New Jersey counties, to capture important heterogeneities of COVID-19 outcomes across the State. The models were calibrated using confirmed daily deaths and SQMC optimization and subsequently applied in predictive and exploratory modes. The predictions achieved good agreement between modeled and reported death data; counterfactual analysis was performed to assess the effectiveness of layered interventions on reducing exposures to SARS-CoV-2 and thereby fatality of COVID-19. The modeling analysis of the reduction in exposures to SARS-CoV-2 achieved through concurrent social distancing and face-mask wearing estimated that 357 [IQR (290, 429)] deaths per 100,000 people were averted.

Biomonitoring: A tool to assess PFNA body burdens and evaluate the effectiveness of drinking water intervention for communities in New Jersey.

Elevated perfluorononanoic acid (PFNA) levels, one of many manmade per- and polyfluoroalkyl substances (PFAS), were detected in public water systems/private wells in New Jersey communities. Interventions to end exposure through drinking water were carried out from 2014 to 2016. To evaluate the effectiveness of interventions, a community biomonitoring study was conducted for the communities between 2017 and 2020. A convenience sampling design was used with 120 participants in Year 1 between ages of 20-74 who consumed PFNA-contaminated water. Three blood samples, one year apart, were drawn from each participant and completed for 99 participants. Separated serum samples were measured for 12 PFAS including PFNA. Questionnaires were administered to collect information on demographics and potential sources. Drinking water and house dust collected at the first visit were analyzed for 14 PFAS including PFNA. The PFNA sera levels (Year 1) found 84 out of 120 (70%) participants were higher than the 95th percentile of a nationally representative sample of US adults (NHANES2015-16). Current drinking water and house dust were not significant contributing sources for the study participants. On average, PFNA sera levels were 12 ± 16% (Year 2) and 27 ± 16% (Year 3) lower than the level measured in Year 1 (p < 0.01). The PFNA half-life was estimated around 3.52 years, using a mixed model from 68 high-exposed participants (>95th percentile of NHANES2015-16) with controlling for physiological covariates. The decline in adult serum PFNA levels seen in the years following a community drinking water intervention suggests the intervention effectively reduced PFNA exposure via drinking water.

A hybrid approach to predict daily NO2 concentrations at city block scale.

Estimating the ambient concentration of nitrogen dioxide (NO2) is challenging because NO2 generated by local fossil fuel combustion varies greatly in concentration across space and time. This study demonstrates an integrated hybrid approach combining dispersion modeling and land use regression (LUR) to predict daily NO2 concentrations at a high spatial resolution (e.g., 50 m) in the New York tri-state area. The daily concentration of traffic-related NO2 was estimated at the Environmental Protection Agency's NO2 monitoring sites in the study area for the years 2015-2017, using the Research LINE source (R-LINE) model with inputs of traffic data provided by the Highway Performance and Management System and meteorological data provided by the NOAA Integrated Surface Database. We used the R-LINE-predicted daily concentrations of NO2 to build mixed-effects regression models, including additional variables representing land use features, geographic characteristics, weather, and other predictors. The mixed model was selected by the Elastic Net method. Each model's performance was evaluated using the out-of-sample coefficient of determination (R2) and the square root of mean squared error (RMSE) from ten-fold cross-validation (CV). The mixed model showed a good prediction performance (CV R2: 0.75-0.79, RMSE: 3.9-4.0 ppb). R-LINE outputs improved the overall, spatial, and temporal CV R2 by 10.0%, 18.9% and 7.7% respectively. Given the output of R-LINE is point-based and has a flexible spatial resolution, this hybrid approach allows prediction of daily NO2 at an extremely high spatial resolution such as city blocks.

A generalized physiologically-based toxicokinetic modeling system for chemical mixtures containing metals.

BACKGROUND: Humans are routinely and concurrently exposed to multiple toxic chemicals, including various metals and organics, often at levels that can cause adverse and potentially synergistic effects. However, toxicokinetic modeling studies of exposures to these chemicals are typically performed on a single chemical basis. Furthermore, the attributes of available models for individual chemicals are commonly estimated specifically for the compound studied. As a result, the available models usually have parameters and even structures that are not consistent or compatible across the range of chemicals of concern. This fact precludes the systematic consideration of synergistic effects, and may also lead to inconsistencies in calculations of co-occurring exposures and corresponding risks. There is a need, therefore, for a consistent modeling framework that would allow the systematic study of cumulative risks from complex mixtures of contaminants. METHODS: A Generalized Toxicokinetic Modeling system for Mixtures (GTMM) was developed and evaluated with case studies. The GTMM is physiologically-based and uses a consistent, chemical-independent physiological description for integrating widely varying toxicokinetic models. It is modular and can be directly "mapped" to individual toxicokinetic models, while maintaining physiological consistency across different chemicals. Interaction effects of complex mixtures can be directly incorporated into the GTMM. CONCLUSIONS: The application of GTMM to different individual metals and metal compounds showed that it explains available observational data as well as replicates the results from models that have been optimized for individual chemicals. The GTMM also made it feasible to model toxicokinetics of complex, interacting mixtures of multiple metals and nonmetals in humans, based on available literature information. The GTMM provides a central component in the development of a "source-to-dose-to-effect" framework for modeling population health risks from environmental contaminants. As new data become available on interactions of multiple chemicals, the GTMM can be iteratively parameterized to improve mechanistic understanding of human health risks from exposures to complex mixtures of chemicals.

Comparison of Machine Learning and Land Use Regression for fine scale spatiotemporal estimation of ambient air pollution: Modeling ozone concentrations across the contiguous United States.

BACKGROUND: Spatial linear Land-Use Regression (LUR) is commonly used for long-term modeling of air pollution in support of exposure and epidemiological assessments. Machine Learning (ML) methods in conjunction with spatiotemporal modeling can provide more flexible exposure-relevant metrics and have been studied using different model structures. There is however a lack of comparisons of methods available within these two modeling frameworks, that can guide model/algorithm selection in air quality epidemiology. OBJECTIVE: The present study compares thirteen algorithms for spatial/spatiotemporal modeling applied for daily maxima of 8-hour running averages of ambient ozone concentrations at spatial resolutions corresponding to census tracts, to support estimation of annual ozone design values across the contiguous US. These algorithms were selected from nine representative categories and trained using predictors that included chemistry-transport model predictions, meteorological factors, land use and land cover, and stationary and mobile emissions. METHODS: To obtain the best predictive performance, model structures were optimized through a repeated coarse/fine grid search with expert knowledge. Six target-oriented validation strategies were used to prevent overfitting and avoid over-optimistic model evaluation results. In order to take full advantage of the power of different algorithms, we introduced tuning sample weights in spatiotemporal modeling to ensure predictive accuracy of peak concentrations, that is crucial for exposure assessments. In spatial modeling, four interpretation and visualization tools were introduced to explain predictions from different algorithms. RESULTS: Nonlinear ML methods achieved higher prediction accuracy than linear LUR, and the improvements were more significant for spatiotemporal modeling (nearly 10%-40% decrease of predicted RMSE). By tuning the sample weights, spatiotemporal models can predict concentrations used to calculate ozone design values that are comparable or even better than spatial models (nearly 30% decrease of cross-validated RMSE). We visualized the underlying nonlinear relationships, heterogeneous associations and complex interactions from the two best performing ML algorithms, i.e., Random Forest and Extreme Gradient Boosting, and found that the complex patterns were relatively less significant with respect to model accuracy for spatial modeling. CONCLUSION: Machine Learning can provide estimates that are actually more interpretable and practical than linear regression to improve accuracy in modeling human exposures. A careful design of hyperparameter tuning and flexible data splitting and validations is crucial to obtain reliable and stable results. Desirable/successful nonlinear models are expected to capture similar nonlinear patterns and interactions using different ML algorithms.