Global Land Use Regression Model for Nitrogen Dioxide Air Pollution

Premature mortality due to air pollution in European cities: a health impact assessment

BACKGROUND

Ambient air pollution is a major environmental cause of morbidity and mortality worldwide. Cities are generally hotspots for air pollution and disease. However, the exact extent of the health effects of air pollution at the city level is still largely unknown. We aimed to estimate the proportion of annual preventable deaths due to air pollution in almost 1000 cities in Europe.

METHODS

We did a quantitative health impact assessment for the year 2015 to estimate the effect of air pollution exposure (PM_2·5 and NO₂) on natural-cause mortality for adult residents (aged ≥20 years) in 969 cities and 47 greater cities in Europe. We retrieved the cities and greater cities from the Urban Audit 2018 dataset and did the analysis at a 250 m grid cell level for 2015 data based on the global human settlement layer residential population. We estimated the annual premature mortality burden preventable if the WHO recommended values (ie, 10 μg/m³ for PM_2·5 and 40 μg/m³ for NO₂) were achieved and if air pollution concentrations were reduced to the lowest values measured in 2015 in European cities (ie, 3·7 μg/m³ for PM_2·5 and 3·5 μg/m³ for NO₂). We clustered and ranked the cities on the basis of population and age-standardised mortality burden associated with air pollution exposure. In addition, we did several uncertainty and sensitivity analyses to test the robustness of our estimates.

FINDINGS

Compliance with WHO air pollution guidelines could prevent 51 213 (95% CI 34 036-68 682) deaths per year for PM_2·5 exposure and 900 (0-2476) deaths per year for NO₂ exposure. The reduction of air pollution to the lowest measured concentrations could prevent 124 729 (83 332-166 535) deaths per year for PM_2·5 exposure and 79 435 (0-215 165) deaths per year for NO₂ exposure. A great variability in the preventable mortality burden was observed by city, ranging from 0 to 202 deaths per 100 000 population for PM_2·5 and from 0 to 73 deaths for NO₂ per 100 000 population when the lowest measured concentrations were considered. The highest PM_2·5 mortality burden was estimated for cities in the Po Valley (northern Italy), Poland, and Czech Republic. The highest NO₂ mortality burden was estimated for large cities and capital cities in western and southern Europe. Sensitivity analyses showed that the results were particularly sensitive to the choice of the exposure response function, but less so to the choice of baseline mortality values and exposure assessment method.

INTERPRETATION

A considerable proportion of premature deaths in European cities could be avoided annually by lowering air pollution concentrations, particularly below WHO guidelines. The mortality burden varied considerably between European cities, indicating where policy actions are more urgently needed to reduce air pollution and achieve sustainable, liveable, and healthy communities. Current guidelines should be revised and air pollution concentrations should be reduced further to achieve greater protection of health in cities.

FUNDING

Spanish Ministry of Science and Innovation, Internal ISGlobal fund.

Estimating the Daily NO2 Concentration with High Spatial Resolution in the Beijing–Tianjin–Hebei Region Using an Ensemble Learning Model

Nitrogen dioxide (NO2) is an important pollutant related to human activities, which has short-term and long-term effects on human health. An ensemble learning model was constructed and applied to estimate daily NO2 concentrations in the Beijing–Tianjin–Hebei region between 2010 and 2016. A variety of predictive variables included satellite-based troposphere NO2 vertical column concentration, meteorology, elevation, gross domestic product (GDP), population, land-use variables, and road network. The ensemble learning model achieved two things: a 0.01° × 0.01° grid resolution and the estimation of historical data for the years 2010–2013. The ensemble model showed good performance, whereby the R2 of tenfold cross-validation was 0.72 and the R2 of test validation was 0.71. Meteorological hysteretic effects were incorporated into the model, where the one-day lagged boundary layer height contributed the most. The annual NO2 estimation showed little change from 2010 to 2016. The seasonal NO2 estimation from highest to lowest occurred in winter, autumn, spring, and summer. In the annual maps and seasonal maps, the NO2 estimations in the northwest region were lower than those in the southeast region, and there was a heavily polluted band in the south of the Taihang Mountains. In coastal areas, the annual NO2 estimations were higher than the NO2 monitored values. The drawback of the model is underestimation at high values and overestimation at low values. This study indicates that the ensemble learning model has excellent performance in the simulation of NO2 with high spatial and temporal resolution. Furthermore, the research framework in this study can be a generally applied for drawing implications for other regions, especially for other cities in China.

Using Street View Imagery to Predict Street-Level Particulate Air Pollution

Land-use regression (LUR) models are frequently applied to estimate spatial patterns of air pollution. Traditional LUR often relies on fixed-site measurements and GIS-derived variables with limited spatial resolution. We present an approach that leverages Google Street View (GSV) imagery to predict street-level particulate air pollution (i.e., black carbon [BC] and particle number [PN] concentrations). We developed empirical models based on mobile monitoring data and features extracted from ∼52 500 GSV images using a deep learning model. We tested theory- and data-driven feature selection methods as well as models using images within varying buffer sizes (50-2000 m). Compared to LUR models with traditional variables, our models achieved similar model performance using the street-level predictors while also identifying additional potential hotspots. Adjusted R² (10-fold CV R²) with integrated feature selection was 0.57-0.64 (0.50-0.57) and 0.65-0.73 (0.61-0.66) for BC and PN models, respectively. Models using only features near the measurement locations (i.e., GSV images within 250 m) explained ∼50% of air pollution variability, indicating PN and BC are strongly affected by the street-level built environment. Our results suggest that GSV imagery, processed with computer vision techniques, is a promising data source to develop LUR models with high spatial resolution and consistent predictor variables across administrative boundaries.

A Satellite-Based Land Use Regression Model of Ambient NO2 with High Spatial Resolution in a Chinese City

Previous studies have reported that intra-urban variability of NO2 concentrations is even higher than inter-urban variability. In recent years, an increasing number of studies have developed satellite-derived land use regression (LUR) models to predict ground-level NO2 concentrations, though only a few have been conducted at a city scale. In this study, we developed a satellite-derived LUR model to predict seasonal NO2 concentrations at a city scale by including satellite-retrieved NO2 tropospheric column density, population density, traffic indicators, and NOx emission data. The R2 of model fitting and 10-fold cross validation were 0.70 and 0.61 for the satellite-derived seasonal LUR model, respectively. The satellite-based LUR model captured seasonal patterns and fine gradients of NO2 variations at a 100 m × 100 m resolution and demonstrated that NO2 pollution in winter is 1.46 times higher than that in summer. NO2 concentrations declined significantly with increasing distance from roads and with increasing distance from the city center. In Suzhou, 84% of the total population lived in areas with NO2 concentrations exceeding the annual-mean standard at 40 μg/m3 in 2014. This study demonstrated that satellite-retrieved data could help increase the accuracy and temporal resolution of the traditional LUR models at a city scale. This application could support exposure assessment at a high resolution for future epidemiological studies and policy development pertaining to air quality control.

Nitrogen dioxide exposures from LPG stoves in a cleaner-cooking intervention trial

BACKGROUND

Liquefied petroleum gas (LPG) stoves have been promoted in low- and middle-income countries (LMICs) as a clean energy alternative to biomass burning cookstoves.

OBJECTIVE

We sought to characterize kitchen area concentrations and personal exposures to nitrogen dioxide (NO₂) within a randomized controlled trial in the Peruvian Andes. The intervention included the provision of an LPG stove and continuous fuel distribution with behavioral messaging to maximize compliance.

METHODS

We measured 48-hour kitchen area NO₂ concentrations at high temporal resolution in homes of 50 intervention participants and 50 control participants longitudinally within a biomass-to-LPG intervention trial. We also collected 48-hour mean personal exposures to NO₂ among a subsample of 16 intervention and 9 control participants. We monitored LPG and biomass stove use continuously throughout the trial.

RESULTS

In 367 post-intervention 24-hour kitchen area samples of 96 participants' homes, geometric mean (GM) highest hourly NO₂ concentration was 138 ppb (geometric standard deviation [GSD] 2.1) in the LPG intervention group and 450 ppb (GSD 3.1) in the biomass control group. Post-intervention 24-hour mean NO₂ concentrations were a GM of 43 ppb (GSD 1.7) in the intervention group and 77 ppb (GSD 2.0) in the control group. Kitchen area NO₂ concentrations exceeded the WHO indoor hourly guideline an average of 1.3 h per day among LPG intervention participants. GM 48-hour personal exposure to NO₂ was 5 ppb (GSD 2.4) among 35 48-hour samples of 16 participants in the intervention group and 16 ppb (GSD 2.3) among 21 samples of 9 participants in the control group.

DISCUSSION

In a biomass-to-LPG intervention trial in Peru, kitchen area NO₂ concentrations were substantially lower within the LPG intervention group compared to the biomass-using control group. However, within the LPG intervention group, 69% of 24-hour kitchen area samples exceeded WHO indoor annual guidelines and 47% of samples exceeded WHO indoor hourly guidelines. Forty-eight-hour NO₂ personal exposure was below WHO indoor annual guidelines for most participants in the LPG intervention group, and we did not measure personal exposure at high temporal resolution to assess exposure to cooking-related indoor concentration peaks. Further research is warranted to understand the potential health risks of LPG-related NO₂ emissions and inform current campaigns which promote LPG as a clean-cooking option.

Potential for developing independent daytime/nighttime LUR models based on short-term mobile monitoring to improve model performance

Land use regression model (LUR) is a widespread method for predicting air pollution exposure. Few studies have explored the performance of independently developed daytime/nighttime LUR models. In this study, fine particulate matter (PM_2.5), inhalable particulate matter (PM₁₀), and nitrogen dioxide (NO₂) concentrations were measured by mobile monitoring during non-heating and heating seasons in Taiyuan. Pollutant concentrations were higher in the nighttime than the daytime, and higher in the heating season than the non-heating season. Daytime/nighttime and full-day LUR models were developed and validated for each pollutant to examine variations in model performance. Adjusted coefficients of determination (adjusted R²) for the LUR models ranged from 0.53-0.87 (PM_2.5), 0.53-0.85 (PM₁₀), and 0.33-0.67 (NO₂). The performance of the daytime/nighttime LUR models for PM_2.5 and PM₁₀ was better than that of the full-day models according to the results of model adjusted R² and validation R². Consistent results were confirmed in the non-heating and heating seasons. Effectiveness of developing independent daytime/nighttime models for NO₂ to improve performance was limited. Surfaces based on the daytime/nighttime models revealed variations in concentrations and spatial distribution. In conclusion, the independent development of daytime/nighttime LUR models for PM_2.5/PM₁₀ has the potential to replace full-day models for better model performance. The modeling strategy is consistent with the residential activity patterns and contributes to achieving reliable exposure predictions for PM_2.5 and PM₁₀. Nighttime could be a critical exposure period, due to high pollutant concentrations.

Residential traffic exposure and lymphohematopoietic malignancies among children in the city of São Paulo, Brazil: An ecological study

BACKGROUND

Despite widespread evidence that air pollution is carcinogenic, there is little evidence from low-middle income countries, especially related to childhood malignancies. We examined the role of traffic related pollution on lymphohematopoietic malignancies among under-14 s in Sao Paulo.

METHODS

All incident cases between 2002 and 2011 were collected from a population-based registry. Exposures were assigned on residential address at diagnosis via traffic density database (for the year 2008) and a satellite derived NO₂ land use regression model (averaged between 1997 and 2011). Incidence rate ratios (IRRs) were calculated via Poisson Regression adjusted by age, gender and socioeconomic status (SES), with additional stratification by SES.

RESULTS

A positive association between traffic and NO₂ with some lymphohematopoietic malignancies was observed with the degree of effect differing by SES. For example, lymphoid leukemia IRRs in the lower SES group were 1.21 (95 % CI: 1.06, 1.39) for traffic density and 1.38 (95 % CI: 1.13, 1.68) for NO₂. In the higher group they were 1.06 (95 % CI: 1.00, 1.14) and 1.37 (95 % CI: 1.16, 1.62).

CONCLUSION

NO₂ and traffic density were associated with Hodgkin lymphoma and lymphoid leukemia among children in São Paulo. Differing IRRs by gender and SES group indicate differences in underlying risk and/or exposure profiles.

A feasibility study on the association between residential greenness and neurocognitive function in middle-aged Bulgarians

Recent research has indicated that exposure to residential vegetation ("greenness") may be protective against cognitive decline and may support the integrity of the corresponding brain structures. However, not much is known about these effects, especially in less affluent countries and in middle-aged populations. In this study, we investigated the associations between greenness and neurocognitive function. We used a convenience sample of 112 middle-aged Bulgarians and two cognitive tests: the Consortium to Establish a Registry for Alzheimer's Disease Neuropsychological Battery (CERAD-NB) and the Montreal Cognitive Assessment (MoCA). In addition, structural brain imaging data were available for 25 participants. Participants' home address was used to link cognition scores to the normalised difference vegetation index (NDVI), a measure of overall neighbourhood vegetation level (radii from 100 to 1,000 m). Results indicated that higher NDVI was consistently associated with higher CERAD-NB and MoCA scores across radial buffers and adjustment scenarios. Lower waist circumference mediated the effect of NDVI on CERAD-NB. NDVI100-m was positively associated with average cortical thickness across both hemispheres, but these correlations turned marginally significant (P<0.1) after correction for false discovery rate due to multiple comparisons. In conclusion, living in a greener neighbourhood might be associated with better cognitive function in middle-aged Bulgarians, with lower central adiposity partially accounting for this effect. Tentative evidence suggests that greenness might also contribute to structural integrity in the brain regions regulating cognitive functions. Future research should build upon our findings and investigate larger and more representative population groups.

Pathways and contingencies linking road traffic noise to annoyance, noise sensitivity, and mental Ill-Health

Context:

Traffic noise may contribute to depression and anxiety through higher noise annoyance (NA). However, little is known about noise sensitivity (NS) and mental health status as contextual factors.

Objective:

We tested three hypotheses: (1) Traffic noise is associated with mental ill-health through higher NA; (2) Mental ill-health and NS moderate the association between traffic noise and NA; and (3) NS moderates the indirect effect of traffic noise on mental ill-health.

Subjects and Methods:

We used a convenience sample of 437 undergraduate students from the Medical University in Plovdiv, Bulgaria (mean age 21 years; 35% male). Residential road traffic noise (L_Aeq; day equivalent noise level) was calculated using a land use regression model. Depression and anxiety symptoms were measured with the Patient Health Questionnaire 9-item (PHQ-9) and the Generalized Anxiety Disorder 7-item (GAD-7) scale, respectively. NA was measured using a 5-point verbal scale. The Noise Sensitivity Scale Short Form (NSS-SF) was used to measure NS. To investigate how these variables intertwine, we conducted mediation, moderation and moderated mediation analyses.

Results:

L_Aeq was indirectly associated with higher PHQ-9/GAD-7 scores through higher NA, but only in the low NS group. The relationship between L_Aeq and NA was stronger in students reporting depression/anxiety. While high NS was associated with high NA even at low noise levels, L_Aeq contributed to NA only in students low on NS.

Conclusions:

We found complex conditional relationships between traffic noise, annoyance and mental ill-health. Understanding respective vulnerability profiles within the community could aid noise policy and increase efficacy of interventions.

Evaluation of different methods and data sources to optimise modelling of NO2 at a global scale

BACKGROUND

In countries where air pollution stations are unavailable or scarce, station measurements from other countries and atmospheric remote sensing could jointly provide information to estimate ambient air quality at a sufficiently fine resolution to study the relationship between air pollution exposure and health. Predicting NO₂ concentration globally with sufficient spatial and temporal resolution and accuracy for health studies is, however, not a trivial task. Challenges are data deficiency, in terms of NO₂ measurements and NO₂ predictors, and the development of a statistical model that can typify the regional and continental differences, such as traffic regulations, energy sources, and local weather.

OBJECTIVE

We investigated the feasibility of mapping daytime and nighttime NO₂ globally at a high spatial resolution (25 m), by including TROPOMI (TROPOspheric Monitoring Instrument) data and comparing various statistical learning techniques.

METHOD

We separated daytime (7:00 am - 9:59 pm) and nighttime (10:00 pm - 6:59 am) based on the local times. To study if one should build models for each country separately, national models in 4 selected countries (the US, China, Germany, Spain) were developed. We build the models for 2017 and used 3636 stations. Seven statistical learning techniques were applied and the impact of the predictors, model fitting, and predicting accuracy was compared between different techniques, national models, national and global models, and models with and without including the NO₂ vertical column density retrieved from TROPOMI.

RESULT AND CONCLUSION

The ensemble tree-based methods obtained higher accuracy compared to the linear regression-based methods in national and global models. The global tree-based methods obtained similar accuracy to national models. Different spatial prediction patterns are observed even when the prediction accuracy is very similar. Separating between day and night can be important for more accurate air pollution exposure assessment. The TROPOMI variable is ranked as one of the most important variables in the statistical learning techniques but adding it to global models that contain other precedent remote sensing products does not improve the prediction accuracy.

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.

High-resolution spatiotemporal measurement of air and environmental noise pollution in Sub-Saharan African cities: Pathways to Equitable Health Cities Study protocol for Accra, Ghana

INTRODUCTION

Air and noise pollution are emerging environmental health hazards in African cities, with potentially complex spatial and temporal patterns. Limited local data are a barrier to the formulation and evaluation of policies to reduce air and noise pollution.

METHODS AND ANALYSIS

We designed a year-long measurement campaign to characterise air and noise pollution and their sources at high-resolution within the Greater Accra Metropolitan Area (GAMA), Ghana. Our design uses a combination of fixed (year-long, n=10) and rotating (week-long, n =~130) sites, selected to represent a range of land uses and source influences (eg, background, road traffic, commercial, industrial and residential areas, and various neighbourhood socioeconomic classes). We will collect data on fine particulate matter (PM_2.5), nitrogen oxides (NO_x), weather variables, sound (noise level and audio) along with street-level time-lapse images. We deploy low-cost, low-power, lightweight monitoring devices that are robust, socially unobtrusive, and able to function in Sub-Saharan African (SSA) climate. We will use state-of-the-art methods, including spatial statistics, deep/machine learning, and processed-based emissions modelling, to capture highly resolved temporal and spatial variations in pollution levels across the GAMA and to identify their potential sources. This protocol can serve as a prototype for other SSA cities.

ETHICS AND DISSEMINATION

This environmental study was deemed exempt from full ethics review at Imperial College London and the University of Massachusetts Amherst; it was approved by the University of Ghana Ethics Committee (ECH 149/18-19). This protocol is designed to be implementable in SSA cities to map environmental pollution to inform urban planning decisions to reduce health harming exposures to air and noise pollution. It will be disseminated through local stakeholder engagement (public and private sectors), peer-reviewed publications, contribution to policy documents, media, and conference presentations.

Observing Nitrogen Dioxide Air Pollution Inequality Using High-Spatial-Resolution Remote Sensing Measurements in Houston, Texas

Houston, Texas is a major U.S. urban and industrial area where poor air quality is unevenly distributed and a disproportionate share is located in low-income, non-white, and Hispanic neighborhoods. We have traditionally lacked city-wide observations to fully describe these spatial heterogeneities in Houston and in cities globally, especially for reactive gases like nitrogen dioxide (NO₂). Here, we analyze novel high-spatial-resolution (250 m × 500 m) NO₂ vertical columns measured by the NASA GCAS airborne spectrometer as part of the September-2013 NASA DISCOVER-AQ mission and discuss differences in population-weighted NO₂ at the census-tract level. Based on the average of 35 repeated flight circuits, we find 37 ± 6% higher NO₂ for non-whites and Hispanics living in low-income tracts (LIN) compared to whites living in high-income tracts (HIW) and report NO₂ disparities separately by race ethnicity (11-32%) and poverty status (15-28%). We observe substantial time-of-day and day-to-day variability in LIN-HIW NO₂ differences (and in other metrics) driven by the greater prevalence of NO_x (≡NO + NO₂) emission sources in low-income, non-white, and Hispanic neighborhoods. We evaluate measurements from the recently launched satellite sensor TROPOMI (3.5 km × 7 km at nadir), averaged to 0.01° × 0.01° using physics-based oversampling, and demonstrate that TROPOMI resolves similar relative, but not absolute, tract-level differences compared to GCAS. We utilize the high-resolution FIVE and NEI NO_x inventories, plus one year of TROPOMI weekday-weekend variability, to attribute tract-level NO₂ disparities to industrial sources and heavy-duty diesel trucking. We show that GCAS and TROPOMI spatial patterns correspond to the surface patterns measured using aircraft profiling and surface monitors. We discuss opportunities for satellite remote sensing to inform decision making in cities generally.

Remote Sensing Estimation of Regional NO2 via Space-Time Neural Networks

Nitrogen dioxide (NO2) is an essential air pollutant related to adverse health effects. A space-time neural network model is developed for the estimation of ground-level NO2 in this study by integrating ground NO2 station measurements, satellite NO2 products, simulation data, and other auxiliary data. Specifically, a geographically and temporally weighted generalized regression neural network (GTW-GRNN) model is used with the advantage to consider the spatiotemporal variations of the relationship between NO2 and influencing factors in a nonlinear neural network framework. The case study across the Wuhan urban agglomeration (WUA), China, indicates that the GTW-GRNN model outperforms the widely used geographically and temporally weighted regression (GTWR), with the site-based cross-validation R2 value increasing by 0.08 (from 0.61 to 0.69). Besides, the comparison between the GTW-GRNN and original global GRNN models shows that considering the spatiotemporal variations in GRNN modeling can boost estimation accuracy. All these results demonstrate that the GTW-GRNN based NO2 estimation framework will be of great use for remote sensing of ground-level NO2 concentrations.

Nitrogen Dioxide Inhalation Exposures Induce Cardiac Mitochondrial Reactive Oxygen Species Production, Impair Mitochondrial Function and Promote Coronary Endothelial Dysfunction

Traffic air pollution is a major health problem and is recognized as an important risk factor for cardiovascular (CV) diseases. In a previous experimental study, we showed that diesel exhaust (DE) exposures induced cardiac mitochondrial and CV dysfunctions associated with the gaseous phase. Here, we hypothesized that NO₂ exposures to levels close to those found in DE induce a mitochondrial reactive oxygen species (ROS) production, which contribute to an endothelial dysfunction, an early indicator for numerous CV diseases. For this, we studied the effects of NO₂ on ROS production and its impacts on the mitochondrial, coronary endothelial and cardiac functions, after acute (one single exposure) and repeated (three h/day, five days/week for three weeks) exposures in Wistar rats. Acute NO₂ exposure induced an early but reversible mitochondrial ROS production. This event was isolated since neither mitochondrial function nor endothelial function were impaired, whereas cardiac function assessment showed a reversible left ventricular dysfunction. Conversely, after three weeks of exposure this alteration was accompanied by a cardiac mitochondrial dysfunction highlighted by an alteration of adenosine triphosphate (ATP) synthesis and oxidative phosphorylation and an increase in mitochondrial ROS production. Moreover, repeated NO₂ exposures promoted endothelial dysfunction of the coronary arteries, as shown by reduced acetylcholine-induced vasodilatation, which was due, at least partially, to a superoxide-dependent decrease of nitric oxide (NO) bioavailability. This study shows that NO₂ exposures impair cardiac mitochondrial function, which, in conjunction with coronary endothelial dysfunction, contributes to cardiac dysfunction. Together, these results clearly identify NO₂ as a probable risk factor in ischemic heart diseases.

Analytical approaches to testing pathways linking greenspace to health: A scoping review of the empirical literature

BACKGROUND

Inadequate translation from theoretical to statistical models of the greenspace - health relationship may lead to incorrect conclusions about the importance of some pathways, which in turn may reduce the effectiveness of public health interventions involving urban greening. In this scoping review we aimed to: (1) summarize the general characteristics of approaches to intervening variable inference (mediation analysis) employed in epidemiological research in the field; (2) identify potential threats to the validity of findings; and (3) propose recommendations for planning, conducting, and reporting mediation analyses.

METHODS

We conducted a scoping review, searching PubMed, Scopus, and Web of Science for peer-reviewed epidemiological studies published by December 31, 2019. The list of potential studies was continuously updated through other sources until March 2020. Narrative presentation of the results was coupled with descriptive summary of study characteristics.

RESULTS

We found 106 studies, most of which were cross-sectional in design. Most studies only had a spatial measure of greenspace. Mental health/well-being was the most commonly studied outcome, and physical activity and air pollution were the most commonly tested intervening variables. Most studies only conducted single mediation analysis, even when multiple potentially intertwined mediators were measured. The analytical approaches used were causal steps, difference-of-coefficients, product-of-coefficients, counterfactual framework, and structural equation modelling (SEM). Bootstrapping was the most commonly used method to construct the 95% CI of the indirect effect. The product-of-coefficients method and SEM as used to investigate serial mediation components were more likely to yield findings of indirect effect. In some cases, the causal steps approach thwarted tests of indirect effect, even though both links in an indirect effect were supported. In most studies, sensitivity analyses and proper methodological discussion of the modelling approach were missing.

CONCLUSIONS

We found a persistent pattern of suboptimal conduct and reporting of mediation analysis in epidemiological studies investigating pathways linking greenspace to health; however, recent years have seen improvements in these respects. Better planning, conduct, and reporting of mediation analyses are warranted.

Nitrogen dioxide exposures from biomass cookstoves in the Peruvian Andes

BACKGROUND

Household air pollution from biomass cookstoves is a major contributor to global morbidity and mortality, yet little is known about exposures to nitrogen dioxide (NO₂ ).

OBJECTIVE

To characterize NO₂ kitchen area concentrations and personal exposures among women with biomass cookstoves in the Peruvian Andes.

METHODS

We measured kitchen area NO₂ concentrations at high-temporal resolution in 100 homes in the Peruvian Andes. We assessed personal exposure to NO₂ in a subsample of 22 women using passive samplers.

RESULTS

Among 97 participants, the geometric mean (GM) highest hourly average NO₂ concentration was 723 ppb (geometric standard deviation (GSD) 2.6) and the GM 24-hour average concentration was 96 ppb (GSD 2.6), 4.4 and 2.9 times greater than WHO indoor hourly (163 ppb) and annual (33 ppb) guidelines, respectively. Compared to the direct-reading instruments, we found similar kitchen area concentrations with 48-hour passive sampler measurements (GM 108 ppb, GSD 3.8). Twenty-seven percent of women had 48-hour mean personal exposures above WHO annual guidelines (GM 18 ppb, GSD 2.3). In univariate analyses, we found that roof, wall, and floor type, as well as higher SES, was associated with lower 24-hour kitchen area NO₂ concentrations.

PRACTICAL IMPLICATIONS

Kitchen area concentrations and personal exposures to NO₂ from biomass cookstoves in the Peruvian Andes far exceed WHO guidelines. More research is warranted to understand the role of this understudied household air pollutant on morbidity and mortality and to inform cleaner-cooking interventions for public health.

Using Satellites to Track Indicators of Global Air Pollution and Climate Change Impacts: Lessons Learned From a NASA-Supported Science-Stakeholder Collaborative

The 2018 NASA Health and Air Quality Applied Science Team (HAQAST) "Indicators" Tiger Team collaboration between NASA-supported scientists and civil society stakeholders aimed to develop satellite-derived global air pollution and climate indicators. This Commentary shares our experience and lessons learned. Together, the team developed methods to track wildfires, dust storms, pollen counts, urban green space, nitrogen dioxide concentrations and asthma burdens, tropospheric ozone concentrations, and urban particulate matter mortality. Participatory knowledge production can lead to more actionable information but requires time, flexibility, and continuous engagement. Ground measurements are still needed for ground truthing, and sustained collaboration over time remains a challenge.

Bayesian geostatistical modelling of high-resolution NO2 exposure in Europe combining data from monitors, satellites and chemical transport models

Bayesian geostatistical regression (GR) models estimate air pollution exposure at high spatial resolution, quantify the prediction uncertainty and provide probabilistic inference on the exceedance of air quality thresholds. However, due to high computational burden, previous GR models have provided gridded ambient nitrogen dioxide (NO₂) concentrations at smaller areas of investigation. Here, we applied these models to estimate yearly averaged NO₂ concentrations at 1 km² spatial resolution across 44 European countries, integrating information from in situ monitoring stations, satellites and chemical transport model (CTM) simulations. The tropospheric values of NO₂ derived from the ozone monitoring instrument (OMI) onboard the National Aeronautics and Space Administration's (NASA's) Aura satellite were converted to near ground NO₂ concentration proxies using simulations from the 3-D global CTM (GEOS-Chem) at 0.5° × 0.625°spatial resolution and surface-to-column NO₂ ratios. Simulations from the Ensemble of regional CTMs at spatial resolution of 0.1° × 0.1°were extracted from the Copernicus atmosphere monitoring service (CAMS). The contribution of these covariates to the predictive capability of geostatistical models was for the first time evaluated here through a rigorous model selection procedure along with additional continental high-resolution satellite-derived products, including novel data from the pan-European Copernicus land monitoring service (CLMS). The results have shown that the conversion of columnar NO₂ values to surface quasi-observations yielded models with slightly better predictive ability and lower uncertainty. Nonetheless, the use of higher resolution CAMS-Ensemble simulations as covariates in GR models granted the most accurate surface NO₂ estimates, showing that, in 2016, 16.17 (95% C.I. 6.34-29.96) million people in Europe, representing 2.97% (95% C.I. 1.16% - 5.50%) of the total population, were exposed to levels above the EU directive and WHO air quality guidelines threshold for NO₂. Our estimates are readily available to policy makers and scientists assessing the burden of disease attributable to NO₂ in 2016.

Polycyclic aromatic hydrocarbons in the soils of the Yangtze River Delta Urban Agglomeration, China: Influence of land cover types and urbanization

With the development of urbanization, urban areas have become the main sources and sinks of polycyclic aromatic hydrocarbons (PAHs). The effects of human activities on the behaviors of PAHs in urban agglomerations have attracted significant attention. We collected soil samples (n = 330) to investigate the distribution, composition, and sources of 16 PAHs in the Yangtze River Delta Urban Agglomeration using the land resolution of 24 km × 24 km. The concentrations of Σ₁₆PAHs ranged from 21 to 2034 ng/g, with a median value of 124 ± 338 ng/g. The concentrations of PAHs were highest in impervious surfaces (350 ± 352 ng/g), followed by grassland (259 ± 322 ng/g), cropland (254 ± 341 ng/g), forest (190 ± 303 ng/g), and water (68 ± 34 ng/g). PAHs were dominated by medium-molecular-weight components (4 rings PAHs), followed by PAHs with high-molecular-weight (5-6 rings PAHs) and low-molecular-weight (2-3 rings PAHs) components. Fluoranthene, benzo[a]anthracene and chrysene are three major pollutants in YRDUA. A positive matrix factorization model indicated that fossil fuel combustion, coal combustion and volatilization, vehicle emission, and biomass burning were the main sources of PAHs, contributing 36%, 29%, 22%, and 12% of PAH sources, respectively. Urbanization parameters were positively correlated with PAH concentrations. A land use regression (LUR) model integrated with urbanization parameters showed evidence of the strong relationship between measured PAHs and predicted PAHs. These findings together highlighted that land cover types and human activities intensively influenced the PAHs pollution in the highly urbanized zones.

Availability, access, analysis and dissemination of small-area data

In this era of 'big data', there is growing recognition of the value of environmental, health, social and demographic data for research. Open government data initiatives are growing in number and in terms of content. Remote sensing data are finding widespread use in environmental research, including in low- and middle-income settings. While our ability to study environment and health associations across countries and continents grows, data protection rules and greater patient control over the use of their data present new challenges to using health data in research. Innovative tools that circumvent the need for the physical sharing of data by supporting non-disclosive sharing of information, or that permit spatial analysis without researchers needing access to underlying patient data can be used to support analyses while protecting data confidentiality. User-friendly visualizations, allowing small-area data to be seen and understood by non-expert audiences, are revolutionizing public and researcher interactions with data. The UK Small Area Health Statistics Unit's Environment and Health Atlas for England and Wales, and the US National Environmental Public Health Tracking Network offer good examples. Open data facilitates user-generated outputs, and 'mash-ups', and user-generated inputs from social media, mobile devices and wearable tech are new data streams that will find utility in future studies, and bring novel dimensions with respect to ethical use of small-area data.

Residential greenness, air pollution and psychological well-being among urban residents in Guangzhou, China

China's rapid urbanization has led to an increasing level of exposure to air pollution and a decreasing level of exposure to vegetation among urban populations. Both trends may pose threats to psychological well-being. Previous studies on the interrelationships among greenness, air pollution and psychological well-being rely on exposure measures from remote sensing data, which may fail to accurately capture how people perceive vegetation on the ground. To address this research gap, this study aimed to explore relationships among neighbourhood greenness, air pollution exposure and psychological well-being, using survey data on 1029 adults residing in 35 neighbourhoods in Guangzhou, China. We used the Normalized Difference Vegetation Index (NDVI) and streetscape greenery (SVG) to assess greenery exposure at the neighbourhood level, and we distinguished between trees (SVG-tree) and grasses (SVG-grass) when generating streetscape greenery exposure metrics. We used two objective (PM_2.5 and NO₂ concentrations) measures and one subjective (perceived air pollution) measure to quantify air pollution exposure. We quantified psychological well-being using the World Health Organization Well-Being Index (WHO-5). Results from multilevel structural equation models (SEM) showed that, for parallel mediation models, while the association between SVG-grass and psychological well-being was completely mediated by perceived air pollution and NO₂, the relationship between SVG-tree and psychological well-being was completely mediated by ambient PM_2.5, NO₂ and perceived air pollution. None of three air pollution indicators mediated the association between psychological well-being and NDVI. For serial mediation models, measures of air pollution did not mediate the relationship between NDVI and psychological well-being. While the linkage between SVG-grass and psychological well-being scores was partially mediated by NO₂-perceived air pollution, SVG-tree was partially mediated by both ambient PM_2.5-perceived air pollution and NO₂-perceived air pollution. Our results suggest that street trees may be more related to lower air pollution levels and better mental health than grasses are.

New Approaches to Identifying and Reducing the Global Burden of Disease From Pollution

Pollution from multiple sources causes significant disease and death worldwide. Some sources are legacy, such as heavy metals accumulated in soils, and some are current, such as particulate matter. Because the global burden of disease from pollution is so high, it is important to identify legacy and current sources and to develop and implement effective techniques to reduce human exposure. But many limitations exist in our understanding of the distribution and transport processes of pollutants themselves, as well as the complicated overprint of human behavior and susceptibility. New approaches are being developed to identify and eliminate pollution in multiple environments. Community-scale detection of geogenic arsenic and fluoride in Bangladesh is helping to map the distribution of these harmful elements in drinking water. Biosensors such as bees and their honey are being used to measure heavy metal contamination in cities such as Vancouver and Sydney. Drone-based remote sensors are being used to map metal hot spots in soils from former mining regions in Zambia and Mozambique. The explosion of low-cost air monitors has allowed researchers to build dense air quality sensing networks to capture ephemeral and local releases of harmful materials, building on other developments in personal exposure sensing. And citizen science is helping communities without adequate resources measure their own environments and in this way gain agency in controlling local pollution exposure sources and/or alerting authorities to environmental hazards. The future of GeoHealth will depend on building on these developments and others to protect a growing population from multiple pollution exposure risks.

Concentrations of criteria pollutants in the contiguous U.S., 1979 - 2015: Role of prediction model parsimony in integrated empirical geographic regression

National-scale empirical models for air pollution can include hundreds of geographic variables. The impact of model parsimony (i.e., how model performance differs for a large versus small number of covariates) has not been systematically explored. We aim to (1) build annual-average integrated empirical geographic (IEG) regression models for the contiguous U.S. for six criteria pollutants during 1979–2015; (2) explore systematically the impact on model performance of the number of variables selected for inclusion in a model; and (3) provide publicly available model predictions. We compute annual-average concentrations from regulatory monitoring data for PM₁₀, PM_2.5, NO₂, SO₂, CO, and ozone at all monitoring sites for 1979–2015. We also use ~350 geographic characteristics at each location including measures of traffic, land use, land cover, and satellite-based estimates of air pollution. We then develop IEG models, employing universal kriging and summary factors estimated by partial least squares (PLS) of geographic variables. For all pollutants and years, we compare three approaches for choosing variables to include in the PLS model: (1) no variables, (2) a limited number of variables selected from the full set by forward selection, and (3) all variables. We evaluate model performance using 10-fold cross-validation (CV) using conventional and spatially-clustered test data. Models using 3 to 30 variables selected from the full set generally have the best performance across all pollutants and years (median R² conventional [clustered] CV: 0.66 [0.47]) compared to models with no (0.37 [0]) or all variables (0.64 [0.27]). Concentration estimates for all Census Blocks reveal generally decreasing concentrations over several decades with local heterogeneity. Our findings suggest that national prediction models can be built by empirically selecting only a small number of important variables to provide robust concentration estimates. Model estimates are freely available online.

Developing nitrate-nitrogen transport models using remotely-sensed geospatial data of soil moisture profiles and wet depositions

Nutrients loads in aquatic systems are dynamic and highly influenced by changing the land, soil, and atmospheric conditions. This study enhances water quality modeling by providing novel nitrate transport models using remotely-sensed geospatial data, allowing for dynamic predictions of nitrate loads in watersheds. One factor at a time, sensitivity analysis was employed in the classical nitrate transport model to incorporate the impacts of 1) nitrates in the soil moisture profiles 2) wet deposition of nitrates and 3) the synergistic effects of multiple atmospheric and soil effects on nitrate-nitrogen in catchments. The study found that the effects of soil moisture profiles were dominant than the wet deposition in the evaluation of nitrate-nitrogen in catchments. The addition of nitrates from soil moisture profile, wet deposition and both together effectively increased the annual average nitrates in the Fish River catchment from 0.180 kg/ha to 0.187 kg/ha, 0.396 kg/ha and 0.381 kg/ha respectively. Their additions consistently increased the nitrate loads from spring to winter seasons but exhibited different seasonal trends for soils such as silty sand and fine sand. The models developed in this study can be utilized in water quality assessment tools for effective dynamic predictions of nutrients loads into water bodies.

The high-resolution estimation of sulfur dioxide (SO2) concentration, health effect and monetary costs in Beijing

Severe air pollution episodes with high SO₂ loading have been frequently observed during the last decades in Beijing and have caused a noticeable damage to human health. To advance the spatiotemporal prediction of SO₂ exposure in Beijing, we developed the monthly land use regression (LUR) models using daily SO₂ concentration data collected from 34 monitoring stations during 2016 and 7 categories of potential independent variables (socio-economic factors, traffic and transport, emission source, land use, meteorological data, building morphology and Geographic location) in Beijing. The average adjusted R² of 12 final LUR models was 0.62, and the root-mean-squared error (RMSE) was 4.12 μg/m³. The LOOCV R² and RMSE of LUR models reached 0.56 and 5.43 μg/m³, respectively, suggesting that the LUR models achieved the satisfactory performance. The prediction results suggested that the average SO₂ level in Beijing was 11.06 μg/m³ with the highest one up to 22.49 μg/m³ but the lowest one down to 3.86 μg/m³. The SO₂ exposure showed strong spatial heterogeneity, which was much higher in the southern area than that in the northern in Beijing. The mortality and morbidity due to the excessive SO₂ concentration were estimated to be 73 (95% CI:(38-125)) and 27854 (95% CI:(13852-41659)) cases per year in Beijing, leading to economic cost of 35.76 (95% CI:(16.45-54.06)) and 441.47 (95% CI:(318.31-562.04)) million RMB Yuan in 2016, respectively. This study clarified the intra- and inter-regional transport modeling of the SO₂ pollution in Beijing and supplied an important support for the future air-quality and public health management strategies.

Opportunities and Challenges for Filling the Air Quality Data Gap in Low- and Middle-Income Countries

Given the millions of people suffering from air pollution, filling the air quality monitoring gap in low- and middle-income countries has been recognized as a global challenge. To meet this challenge and make it work will require private enterprise, multiple levels of government, international organizations, academia and civil society to work together toward the common goal of characterizing, understanding better, and then reducing, the air pollution that causes sickness and preventable death for millions of people each year in lowand middle-income countries around the world. This article offers concrete next steps on how to make progress toward increasing air quality monitoring using a combination of emerging technologies, adaptation to country-specific conditions, and building capacity towards the development of lasting institutions.

Improving and Expanding Estimates of the Global Burden of Disease Due to Environmental Health Risk Factors

BACKGROUND

The Global Burden of Disease (GBD) study, coordinated by the Institute for Health Metrics and Evaluation (IHME), produces influential, data-driven estimates of the burden of disease and premature death due to major risk factors. Expanded quantification of disease due to environmental health (EH) risk factors, including climate change, will enhance accuracy of GBD estimates, which will contribute to developing cost-effective policies that promote prevention and achieving Sustainable Development Goals.

OBJECTIVES

We review key aspects of the GBD for the EH community and introduce the Global Burden of Disease-Pollution and Health Initiative (GBD-PHI), which aims to work with IHME and the GBD study to improve estimates of disease burden attributable to EH risk factors and to develop an innovative approach to estimating climate-related disease burden-both current and projected.

METHODS

We discuss strategies for improving GBD quantification of specific EH risk factors, including air pollution, lead, and climate change. We highlight key methodological challenges, including new EH risk factors, notably evidence rating and global exposure assessment.

DISCUSSION

A number of issues present challenges to the scope and accuracy of current GBD estimates for EH risk factors. For air pollution, minimal data exist on the exposure-risk relationships associated with high levels of pollution; epidemiological studies in high pollution regions should be a research priority. For lead, the GBD's current methods do not fully account for lead's impact on neurodevelopment; innovative methods to account for subclinical effects are needed. Decisions on inclusion of additional EH risk-outcome pairs need to be guided by findings of systematic reviews, the size of exposed populations, feasibility of global exposure estimates, and predicted trends in exposures and diseases. Neurotoxicants, endocrine-disrupting chemicals, and climate-related factors should be high priorities for incorporation into upcoming iterations of the GBD study. Enhancing the scope and methods will improve the GBD's estimates and better guide prevention policy. https://doi.org/10.1289/EHP5496.

Predicting Fine-Scale Daily NO2 for 2005-2016 Incorporating OMI Satellite Data Across Switzerland

Nitrogen dioxide (NO₂) remains an important traffic-related pollutant associated with both short- and long-term health effects. We aim to model daily average NO₂ concentrations in Switzerland in a multistage framework with mixed-effect and random forest models to respectively downscale satellite measurements and incorporate local sources. Spatial and temporal predictor variables include data from the Ozone Monitoring Instrument, Copernicus Atmosphere Monitoring Service, land use, and meteorological variables. We derived robust models explaining ∼58% (R² range, 0.56-0.64) of the variation in measured NO₂ concentrations using mixed-effect models at a 1 × 1 km resolution. The random forest models explained ∼73% (R² range, 0.70-0.75) of the overall variation in the residuals at a 100 × 100 m resolution. This is one of the first studies showing the potential of using earth observation data to develop robust models with fine-scale spatial (100 × 100 m) and temporal (daily) variation of NO₂ across Switzerland from 2005 to 2016. The novelty of this study is in demonstrating that methods originally developed for particulate matter can also successfully be applied to NO₂. The predicted NO₂ concentrations will be made available to facilitate health research in Switzerland.

An ensemble-based model of PM2.5 concentration across the contiguous United States with high spatiotemporal resolution

Various approaches have been proposed to model PM2.5 in the recent decade, with satellite-derived aerosol optical depth, land-use variables, chemical transport model predictions, and several meteorological variables as major predictor variables. Our study used an ensemble model that integrated multiple machine learning algorithms and predictor variables to estimate daily PM2.5 at a resolution of 1 km × 1 km across the contiguous United States. We used a generalized additive model that accounted for geographic difference to combine PM2.5 estimates from neural network, random forest, and gradient boosting. The three machine learning algorithms were based on multiple predictor variables, including satellite data, meteorological variables, land-use variables, elevation, chemical transport model predictions, several reanalysis datasets, and others. The model training results from 2000 to 2015 indicated good model performance with a 10-fold cross-validated R2 of 0.86 for daily PM2.5 predictions. For annual PM2.5 estimates, the cross-validated R2 was 0.89. Our model demonstrated good performance up to 60 μg/m3. Using trained PM2.5 model and predictor variables, we predicted daily PM2.5 from 2000 to 2015 at every 1 km × 1 km grid cell in the contiguous United States. We also used localized land-use variables within 1 km × 1 km grids to downscale PM2.5 predictions to 100 m × 100 m grid cells. To characterize uncertainty, we used meteorological variables, land-use variables, and elevation to model the monthly standard deviation of the difference between daily monitored and predicted PM2.5 for every 1 km × 1 km grid cell. This PM2.5 prediction dataset, including the downscaled and uncertainty predictions, allows epidemiologists to accurately estimate the adverse health effect of PM2.5. Compared with model performance of individual base learners, an ensemble model would achieve a better overall estimation. It is worth exploring other ensemble model formats to synthesize estimations from different models or from different groups to improve overall performance.

Land-Use Regression Modeling of Source-Resolved Fine Particulate Matter Components from Mobile Sampling

This study presents land-use regression (LUR) models for submicron particulate matter (PM₁) components from an urban area. Models are presented for mass concentrations of inorganic species (SO₄, NO₃, NH₄), organic aerosol (OA) factors, and total PM₁. OA is source-apportioned using positive matrix factorization (PMF) of data collected from aerosol mass spectrometry deployed on a mobile laboratory. PMF yielded a three-factor solution: cooking OA (COA), hydrocarbon-like OA (HOA), and less-oxidized oxygenated OA (LO-OOA). This study represents the first time that LUR has been applied to source-resolved OA factors. We sampled a roughly 20 km² area of West Oakland, California, USA, over 1 month (mid-July to mid-August, 2017). The road network of the sampling domain was comprehensively sampled each day using a randomized driving route to minimize temporal and spatial bias. Mobile measurements were aggregated both spatially and temporally for use as discrete spatial observations for LUR model building. LUR model performance was highest for those species with more spatial variability (primary OA factors: COA R² = 0.80, HOA R² = 0.67) and lowest for secondary inorganic species (SO₄ R² = 0.47, NH₄ R² = 0.43) that were more spatially homogeneous. Notably, the stepwise selective LUR algorithm largely selected predictors for primary OA factors that correspond to the associated land-use categories (e.g., cooking land-use variables were selected in cooking-related PM models). This finding appears to be robust, as we demonstrate the predictive link between land-use variables and the corresponding source-resolved PM₁ components through a subsampling analysis.

Spatial variation in the association between NO2 concentrations and shipping emissions in the Red Sea

Air pollution from shipping emissions poses significant health and environmental risks, particularly in the coastal regions. For the first time, this region as one of the busiest seas and most important international shipping lane in the world with significant nitrogen dioxide (NO₂) emissions has been analyzed comprehensively. This paper aims to characterize and quantify the contribution of maritime transport sector emissions to NO₂ concentrations in the Red Sea using local Geographically Weighted Regression (GWR) model in a geographic information system (GIS) environment. Maritime traffic volume was estimated using SaudiSat satellite-based Automatic Identification System (S-AIS) data, and the remotely measured tropospheric NO₂ concentrations data was acquired from the ozone monitoring instrument (OMI) satellite. A significant spatial variation in the NO₂ values was detected across the Red Sea, with values ranging from 4.03 × 10¹⁴ to 41.39 × 10¹⁴ molecules/cm². Most notably, the NO₂ concentrations in international waters were more than double those in the western coastal regions, whereas the concentrations close to seaports were 100% higher than those over international waters. The results indicated that the local GWR model performed significantly better than the global ordinary least squares (OLS) regression model. The GWR model had a strong and significant overall coefficient of determination with an r² of 0.94 (p < 0.005) in comparison to the OLS model with an r² of 0.45 (p < 0.005). Maritime traffic volume and proximity to seaports weighted by shipping activities explained about 94% of the variations of NO₂ concentrations in the Red Sea. The results of this study suggest that the S-AIS data and environmental satellite measurements can be used to assess the impacts of NO₂ concentrations from shipping emissions. These findings should stimulate further research into using additional covariates to explain the NO₂ concentrations in areas near seaports where the standardized residuals are high.

Community greenness and neurobehavioral health in children and adolescents

BACKGROUND

Neighborhood greenness appears to be beneficial to human health. However, there are few studies on the relationship between greenness of children's residential communities and their neurobehavioral health.

OBJECTIVE

In this cross-sectional study, we examined the association between neighborhood greenness of children's residential area and their neurobehavioral health.

METHODS

We used a population-representative sample of school children (n = 1817) from 2012 and 2013 in South Korea. Parents or guardians of children completed the Child Behavior Checklist (CBCL) to assess their children's neurobehavioral health. As a measurement for greenness, the modified soil-adjusted vegetation index (MSAVI) at a 30-m resolution was retrieved from the Landsat satellite data operated by NASA. The MSAVI values were categorized into tertiles (low, moderate, high greenness) and each child was assigned the mean MSAVI within a 1.6-km radius of residence. We applied survey regressions of the CBCL transformed scores on the 3 levels of greenness, controlling for age, sex, physical activity, monthly family income, exposure to second-hand smoke, exposure to NO₂, and blood lead level.

RESULTS

High greenness was associated with a lower Total CBCL score compared to low greenness (-2.33, 95% CI, -4.10 to -0.56). Externalizing Behavior showed a slightly stronger association with greenness than did Internalizing Behavior. The inverse relationship with greenness was strongest for Attention Problems (-1.32; 95% CI: -2.58, -0.06). The magnitudes of the association were strongest when the buffer distance was 1600 m.

CONCLUSIONS

Greenness of residential neighborhood was associated with lower problematic behavior scores in children, especially aggressive behaviors and attention problems.

Comparison of model estimates from an intra-city land use regression model with a national satellite-LUR and a regional Bayesian Maximum Entropy model, in estimating NO2 for a birth cohort in Sydney, Australia

BACKGROUND

Methods for estimating air pollutant exposures for epidemiological studies are becoming more complex in an effort to minimise exposure error and its associated bias. While land use regression (LUR) modelling is now an established method, there has been little comparison between LUR and other recent, more complex estimation methods. Our aim was to develop a LUR model to estimate intra-city exposures to nitrogen dioxide (NO₂) for a Sydney cohort, and to compare those with estimates from a national satellite-based LUR model (Sat-LUR) and a regional Bayesian Maximum Entropy (BME) model.

METHODS

Satellite-based LUR and BME estimates were obtained using existing models. We used methods consistent with the European Study of Cohorts for Air Pollution Effects (ESCAPE) methodology to develop LUR models for NO₂ and NOx. We deployed 46 Ogawa passive samplers across western Sydney during 2013/2014 and acquired data on land use, population density, and traffic volumes for the study area. Annual average NO₂ concentrations for 2013 were estimated for 947 addresses in the study area using the three models: standard LUR, Sat-LUR and a BME model. Agreement between the estimates from the three models was assessed using interclass correlation coefficient (ICC), Bland-Altman methods and correlation analysis (CC).

RESULTS

The NO₂ LUR model predicted 84% of spatial variability in annual mean NO₂ (RMSE: 1.2 ppb; cross-validated R²: 0.82) with predictors of major roads, population and dwelling density, heavy traffic and commercial land use. A separate model was developed that captured 92% of variability in NOx (RMSE 2.3 ppb; cross-validated R²: 0.90). The annual average NO₂ concentrations were 7.31 ppb (SD: 1.91), 7.01 ppb (SD: 1.92) and 7.90 ppb (SD: 1.85), for the LUR, Sat-LUR and BME models respectively. Comparing the standard LUR with Sat-LUR NO₂ cohort estimates, the mean estimates from the LUR were 4% higher than the Sat-LUR estimates, and the ICC was 0.73. The Pearson's correlation coefficients (CC) for the LUR vs Sat-LUR values were r = 0.73 (log-transformed data) and r = 0.69 (untransformed data). Comparison of the NO₂ cohort estimates from the LUR model with the BME blended model indicated that the LUR mean estimates were 8% lower than the BME estimates. The ICC for the LUR vs BME estimates was 0.73. The CC for the logged LUR vs BME estimates was r = 0.73 and for the unlogged estimates was r = 0.69.

CONCLUSIONS

Our LUR models explained a high degree of spatial variability in annual mean NO₂ and NOx in western Sydney. The results indicate very good agreement between the intra-city LUR, national-scale sat-LUR, and regional BME models for estimating NO₂ for a cohort of children residing in Sydney, despite the different data inputs and differences in spatial scales of the models, providing confidence in their use in epidemiological studies.

The establishment of the Household Air Pollution Consortium (HAPCO)

Household air pollution (HAP) is of public health concern with ~3 billion people worldwide (including >15 million in the US) exposed. HAP from coal use is a human lung carcinogen, yet the epidemiological evidence on carcinogenicity of HAP from biomass use, primarily wood, is not conclusive. To robustly assess biomass's carcinogenic potential, prospective studies of individuals experiencing a variety of HAP exposures are needed. We have built a global consortium of 13 prospective cohorts (HAPCO: Household Air Pollution Consortium) that have site- and disease-specific mortality and solid fuel use data, for a combined sample size of 587,257 participants and 57,483 deaths. HAPCO provides a novel opportunity to assess the association of HAP with lung cancer death while controlling for important confounders such as tobacco and outdoor air pollution exposures. HAPCO is also uniquely positioned to determine the risks associated with cancers other than lung as well as non-malignant respiratory and cardiometabolic outcomes, for which prospective epidemiologic research is limited. HAPCO will facilitate research to address public health concerns associated with HAP-attributed exposures by enabling investigators to evaluate sex-specific and smoking status-specific effects under various exposure scenarios.

Development of an environmental health tool linking chemical exposures, physical location and lung function

BACKGROUND

A challenge in environmental health research is collecting robust data sets to facilitate comparisons between personal chemical exposures, the environment and health outcomes. To address this challenge, the Exposure, Location and lung Function (ELF) tool was designed in collaboration with communities that share environmental health concerns. These concerns centered on respiratory health and ambient air quality. The ELF collects exposure to polycyclic aromatic hydrocarbons (PAHs), given their association with diminished lung function. Here, we describe the ELF as a novel environmental health assessment tool.

METHODS

The ELF tool collects chemical exposure for 62 PAHs using passive sampling silicone wristbands, geospatial location data and respiratory lung function measures using a paired hand-held spirometer. The ELF was tested by 10 individuals with mild to moderate asthma for 7 days. Participants wore a wristband each day to collect PAH exposure, carried a cell phone, and performed spirometry daily to collect respiratory health measures. Location data was gathered using the geospatial positioning system technology in an Android cell-phone.

RESULTS

We detected and quantified 31 PAHs across the study population. PAH exposure data showed spatial and temporal sensitivity within and between participants. Location data was used with existing datasets such as the Toxics Release Inventory and the National Oceanic and Atmospheric Administration (NOAA) Hazard Mapping System. Respiratory health outcomes were validated using criteria from the American Thoracic Society with 94% of participant data meeting standards. Finally, the ELF was used with a high degree of compliance (> 90%) by community members.

CONCLUSIONS

The ELF is a novel environmental health assessment tool that allows for personal data collection spanning chemical exposures, location and lung function measures as well as self-reported information.

Biases in the Measurement of Ambient Nitrogen Dioxide (NO2) by Palmes Passive Diffusion Tube: A Review of Current Understanding

Palmes-type passive diffusion tubes (PDTs) are widely used to measure levels of nitrogen dioxide (NO2) in air quality studies. Molecules of NO2 diffuse down the concentration gradient established in the tube by their reactive conversion into nitrite (NO2−) with triethanolamine (TEA) absorbent at the inner end. The relatively low uptake rate for the tube geometry means that exposure-averaged NO2 concentration can be calculated from first principles using the diffusion coefficient, D, for NO2 in air. This review provides a critical assessment of the current understanding of sources and extent of potential bias in NO2 PDT measurements in each of the following methodological stages: preparation of the absorbent; quantification of the absorbed NO2−; deployment in the field; calculation of the exposure-average NO2 concentration from the absorbed NO2−; and assessment of PDT bias through comparison against a chemiluminescence NO2 analyser. The review has revealed strong evidence that PDT measurement of NO2 can be subject to bias from a number of sources. The most significant positive biases are ambient wind flow at the entrance of the tube potentially leading to bias of tens of percent, and within-tube chemical reaction between NO and O3 causing bias up to ~25% at urban background locations, but much less at roadside and rural locations. Sources of potentially significant negative bias are associated with deployment times of several weeks in warm and sunny conditions, and deployments in atmospheres with relative humidities <~75% which causes incomplete conversion of NO2 to NO2−. Evidence suggests that biases (positive or negative) can be introduced by individual laboratories in the PDT preparation and NO2− quantification steps. It is insufficiently acknowledged that the value of D is not accurately known—some controlled chamber experiments can be interpreted as indicating that the value of D currently used is too low, giving rise to a positive bias in PDT-derived NO2 concentration. More than one bias may be present in a given PDT deployment, and because the biases act independently the net effect on PDT NO2 determination is the linear sum of individual biases acting on that deployment. The effect of net bias can be reduced by application of a local “bias adjustment” factor derived from co-locations of PDTs with a chemiluminescence analyser. When this is carried out, the PDT is suitable as an indicative measure of NO2 for air quality assessments. However, it must be recognised that individual PDT deployments may be subject to unknown variation in the bias adjustment factor for that deployment.

Using deep learning to examine street view green and blue spaces and their associations with geriatric depression in Beijing, China

BACKGROUND

Residential green and blue spaces may be therapeutic for the mental health. However, solid evidence on the linkage between exposure to green and blue spaces and mental health among the elderly in non-Western countries is scarce and limited to exposure metrics based on remote sensing images (i.e., land cover and vegetation indices). Such overhead-view measures may fail to capture how people perceive the environment on the site.

OBJECTIVE

This study aimed to compare streetscape metrics derived from street view images with satellite-derived ones for the assessment of green and blue space; and to examine associations between exposure to green and blue spaces as well as geriatric depression in Beijing, China.

METHODS

Questionnaire data on 1190 participants aged 60 or above were analyzed cross-sectionally. Depressive symptoms were assessed through the shortened Geriatric Depression Scale (GDS-15). Streetscape green and blue spaces were extracted from Tencent Street View data by a fully convolutional neural network. Indicators derived from street view images were compared with a satellite-based normalized difference vegetation index (NDVI), a normalized difference water index (NDWI), and those derived from GlobeLand30 land cover data on a neighborhood level. Multilevel regressions with neighborhood-level random effects were fitted to assess correlations between GDS-15 scores and these green and blue spaces exposure metrics.

RESULTS

The average cumulative GDS-15 score was 3.4 (i.e., no depressive symptoms). Metrics of green and blue space derived from street view images were not correlated with satellite-based ones. While NDVI was highly correlated with GlobeLand30 green space, NDWI was moderately correlated with GlobeLand30 blue space. Multilevel regressions showed that both street view green and blue spaces were inversely associated with GDS-15 scores and achieved the highest model goodness-of-fit. No significant associations were found with NDVI, NDWI, and GlobeLand30 green and blue space. Our results passed robustness tests.

CONCLUSION

Our findings provide support that street view green and blue spaces are protective against depression for the elderly in China, yet longitudinal confirmation to infer causality is necessary. Street view and satellite-derived green and blue space measures represent different aspects of natural environments. Both street view data and deep learning are valuable tools for automated environmental exposure assessments for health-related studies.

Spatiotemporally mapping of the relationship between NO2 pollution and urbanization for a megacity in Southwest China during 2005-2016

Nitrogen dioxide (NO₂) significantly contributes to air pollution. Long-term NO₂ exposure is harmful to human health. The NO₂ pollution in China has surpassed developed countries and attracts international attention. To understand the spatial and temporal distributions of NO₂ across Chengdu in Southwest China, a random forest (RF) model was developed based on NO₂ environmental monitoring data, the Ozone Monitoring Instrument (OMI) satellite retrievals, and geographic covariates. The RF model showed good performance with a cross validation R² of 0.77, and a root mean square error (RMSE) of 11.0 μg/m³. The ground-level NO₂ concentrations of Chengdu for 2005-2016 were predicted using the developed model with the multiyear population weighted NO₂ concentration being 41.7 ± 11.7 μg/m³. The predicted NO₂ concentrations exhibited a clear seasonal variation trend with winter being the highest and summer being the lowest. Furthermore, higher NO₂ concentrations in the downtown areas were observed than that in the rural areas indicating the former being attributed to more anthropogenic sources. The population weighted NO₂ concentrations with deseasonlization were relatively high during 2011-2013. The NO₂ concentration increased at a rate of 0.81 μg/m³/year before 2011 (43.4 ± 11.2 μg/m³) and decreased at a rate of -1.03 μg/m³/year after 2013 (44.8 ± 12.8 μg/m³).

Global, national, and urban burdens of paediatric asthma incidence attributable to ambient NO2 pollution: estimates from global datasets

BACKGROUND

Paediatric asthma incidence is associated with exposure to traffic-related air pollution (TRAP), but the TRAP-attributable burden remains poorly quantified. Nitrogen dioxide (NO₂) is a major component and common proxy of TRAP. In this study, we estimated the annual global number of new paediatric asthma cases attributable to NO₂ exposure at a resolution sufficient to resolve intra-urban exposure gradients.

METHODS

We obtained 2015 country-specific and age-group-specific asthma incidence rates from the Institute for Health Metrics and Evaluation for 194 countries and 2015 population counts at a spatial resolution of 250 × 250 m from the Global Human Settlement population grid. We used 2010-12 annual average surface NO₂ concentrations derived from land-use regression at a resolution of 100 × 100 m, and we derived concentration-response functions from relative risk estimates reported in a multinational meta-analysis. We then estimated the NO₂-attributable burden of asthma incidence in children aged 1-18 years in 194 countries and 125 major cities at a resolution of 250 × 250 m.

FINDINGS

Globally, we estimated that 4·0 million (95% uncertainty interval [UI] 1·8-5·2) new paediatric asthma cases could be attributable to NO₂ pollution annually; 64% of these occur in urban centres. This burden accounts for 13% (6-16) of global incidence. Regionally, the greatest burdens of new asthma cases associated with NO₂ exposure per 100 000 children were estimated for Andean Latin America (340 cases per year, 95% UI 150-440), high-income North America (310, 140-400), and high-income Asia Pacific (300, 140-370). Within cities, the greatest burdens of new asthma cases associated with NO₂ exposure per 100 000 children were estimated for Lima, Peru (690 cases per year, 95% UI 330-870); Shanghai, China (650, 340-770); and Bogota, Colombia (580, 270-730). Among 125 major cities, the percentage of new asthma cases attributable to NO₂ pollution ranged from 5·6% (95% UI 2·4-7·4) in Orlu, Nigeria, to 48% (25-57) in Shanghai, China. This contribution exceeded 20% of new asthma cases in 92 cities. We estimated that about 92% of paediatric asthma incidence attributable to NO₂ exposure occurred in areas with annual average NO₂ concentrations lower than the WHO guideline of 21 parts per billion.

INTERPRETATION

Efforts to reduce NO₂ exposure could help prevent a substantial portion of new paediatric asthma cases in both developed and developing countries, and especially in urban areas. Traffic emissions should be a target for exposure-mitigation strategies. The adequacy of the WHO guideline for ambient NO₂ concentrations might need to be revisited.

FUNDING

George Washington University.

Incidence and mortality for respiratory cancer and traffic-related air pollution in São Paulo, Brazil

BACKGROUND

Multiple lines of evidence have associated exposure to ambient air pollution with an increased risk of respiratory malignancies. However, there is a dearth of evidence from low-middle income countries, including those within South America, where the social inequalities are more marked.

OBJECTIVES

To quantify the association between exposures to traffic related air pollution and respiratory cancer incidence and mortality within São Paulo, Brazil. Further, we aim to investigate the role of socioeconomic status (SES) upon these outcomes.

METHODS

Cancer incidence between 2002 and 2011 was derived from the population-based cancer registry. Mortality data (between 2002 and 2013) was derived from the Municipal Health Department. A traffic density database and an annual nitrogen dioxide (NO₂) land use regression model were used as markers of exposure. Age-adjusted Binomial Negative Regression models were developed, stratifying by SES and gender.

RESULTS

We observed an increased rate of respiratory cancer incidence and mortality in association with increased traffic density and NO₂ concentrations, which was higher among those regions with the lowest SES. For cancer mortality and traffic exposure, those in the most deprived region, had an incidence rate ratio (IRR) of 2.19 (95% CI: 1.70, 2.82) when comparing the highest exposure centile (top 90%) to the lowest (lowest 25%). By contrast, in the least deprived area, the IRR for the same exposure contrast was.1.07 (95% CI: 0.95, 1.20). For NO₂ in the most deprived regions, the IRR for cancer mortality in the highest exposed group was 1.44 (95% CI: 1.10, 1.88) while in the least deprived area, the IRR for the highest exposed group was 1.11 (95% CI: 1.01, 1.23).

CONCLUSIONS

Traffic density and NO₂ were associated with an increased rate of respiratory cancer incidence and mortality in São Paulo. Residents from poor regions may suffer more from the impact of traffic air pollution.

A random forest partition model for predicting NO2 concentrations from traffic flow and meteorological conditions

High concentrations of nitrogen dioxide in the air, particularly in heavily urbanised areas, have an adverse effect on many aspects of residents' health (short-term and long-term damage, unpleasant odour and other). A method is proposed for modelling atmospheric NO₂ concentrations in a conurbation, using a partition model M consisting of two separate models: M_L for lower concentration values and M_U for upper values. An advanced data mining technique, that of random forests, is used. This is a method based on machine learning, involving the simultaneous compilation of information from multiple random trees. Using the example of data recorded in Wrocław (Poland) in 2015-2017, an iterative method was applied to determine the boundary concentration y˜ for which the mean absolute deviation error for the partition model attained its lowest value. The resulting model had an R² value of 0.82, compared with 0.60 for a classical random forest model. The importances of the variables in the model M_L, similarly as in the classical case, indicate that the greatest influence on NO₂ concentrations comes from traffic flow, followed by meteorological factors, in particular the wind direction and speed. In the model M_U the importances of the variables are significantly different: while traffic flow still has the greatest impact, the effects of temperature and relative humidity are almost as great. This confirms the justifiability of constructing separate models for low and high pollution concentrations.

Spatial estimation of surface ozone concentrations in Quito Ecuador with remote sensing data, air pollution measurements and meteorological variables

Surface ozone is problematic to air pollution. It influences respiratory health. The air quality monitoring stations measure pollutants as surface ozone, but they are sometimes insufficient or do not have an adequate distribution for understanding the spatial distribution of pollutants in an urban area. In recent years, some projects have found a connection between remote sensing, air quality and health data. In this study, we apply an empirical land use regression (LUR) model to retrieve surface ozone in Quito. The model considers remote sensing data, air pollution measurements and meteorological variables. The objective is to use all available Landsat 8 images from 2014 and the air quality monitoring station data during the same dates of image acquisition. Nineteen input variables were considered, selecting by a stepwise regression and modelling with a partial least square (PLS) regression to avoid multicollinearity. The final surface ozone model includes ten independent variables and presents a coefficient of determination (R²) of 0.768. The model proposed help to understand the spatial concentration of surface ozone in Quito with a better spatial resolution.

Residential green and blue space associated with better mental health: a pilot follow-up study in university students

Previous research has suggested that natural urban environment (green space and blue space) benefit mental health, but only a few longitudinal studies have explored the underlying mechanisms. In this pilot study we aimed to examine mechanisms/variables mediating associations between residential green/blue space and symptoms of anxiety/depression in 109 Bulgarian students from Plovdiv university. The students were followed from the beginning to the end of the school year (October 2017 to May 2018). Residential green space was defined as the mean of the normalised difference vegetation index (NDVI) in circular buffers of 100, 300, and 500 m around their residences. Blue space was assessed based on its presence in the same buffers. Levels of anxiety/depression were assessed using the 12-item General Health Questionnaire. The investigated mediator variables included residential noise (LAeq) and air pollution (NO2), environmental annoyance, perceived restorative quality of the neighbourhood, neighbourhood social cohesion, physical activity, and sleep disturbance. Cross-sectional data (obtained at baseline) showed that higher NDVI correlated with better mental health only indirectly through higher physical activity and restorative quality. Longitudinal (follow-up) data showed improved mental health but no significant effect of mediator variables. Similarly, blue space correlated with better mental health in all models, but physical activity and restorative quality were significant mediator variables only in the cross-sectional analysis. Our findings support that green space and blue space are psychologically restorative features in urban environment. Future research should replicate these findings in the general population and employ longitudinal modelling tailored to the specific mechanisms under study.

A picture tells a thousand…exposures: Opportunities and challenges of deep learning image analyses in exposure science and environmental epidemiology

BACKGROUND

Artificial intelligence (AI) is revolutionizing our world, with applications ranging from medicine to engineering.

OBJECTIVES

Here we discuss the promise, challenges, and probable data sources needed to apply AI in the fields of exposure science and environmental health. In particular, we focus on the use of deep convolutional neural networks to estimate environmental exposures using images and other complementary data sources such as cell phone mobility and social media information.

DISCUSSION

Characterizing the health impacts of multiple spatially-correlated exposures remains a challenge in environmental epidemiology. A shift toward integrated measures that simultaneously capture multiple aspects of the urban built environment could improve efficiency and provide important insights into how our collective environments influence population health. The widespread adoption of AI in exposure science is on the frontier. This will likely result in new ways of understanding environmental impacts on health and may allow for analyses to be efficiently scaled for broad coverage. Image-based convolutional neural networks may also offer a cost-effective means of estimating local environmental exposures in low and middle-income countries where monitoring and surveillance infrastructure is limited. However, suitable databases must first be assembled to train and evaluate these models and these novel approaches should be complemented with traditional exposure metrics.

CONCLUSIONS

The promise of deep learning in environmental health is great and will complement existing measurements for data-rich settings and could enhance the resolution and accuracy of estimates in data poor scenarios. Interdisciplinary partnerships will be needed to fully realize this potential.

Estimation of PM2.5 mortality burden in China with new exposure estimation and local concentration-response function

The estimation of PM_2.5-related mortality is becoming increasingly important. The accuracy of results is largely dependent on the selection of methods for PM_2.5 exposure assessment and Concentration-Response (C-R) function. In this study, PM_2.5 observed data from the China National Environmental Monitoring Center, satellite-derived estimation, widely collected geographic and socioeconomic information variables were applied to develop a national satellite-based Land Use Regression model and evaluate PM_2.5 exposure concentrations within 2013-2015 with the resolution of 1 km × 1 km. Population weighted concentration declined from 72.52 μg/m³ in 2013 to 57.18 μg/m³ in 2015. C-R function is another important section of health effect assessment, but most previous studies used the Integrated Exposure Regression (IER) function which may currently underestimate the excess relative risk of exceeding the exposure range in China. A new Shape Constrained Health Impact Function (SCHIF) method, which was developed from a national cohort of 189,793 Chinese men, was adopted to estimate the PM_2.5-related premature deaths in China. Results showed that 2.19 million (2013), 1.94 million (2014), 1.65 million (2015) premature deaths were attributed to PM_2.5 long-term exposure, different from previous understanding around 1.1-1.7 million. The top three provinces of the highest premature deaths were Henan, Shandong, Sichuan, while the least ones were Tibet, Hainan, Qinghai. The proportions of premature deaths caused by specific diseases were 53.2% for stroke, 20.5% for ischemic heart disease, 16.8% for chronic obstructive pulmonary disease and 9.5% for lung cancer. IER function was also used to calculate PM_2.5-related premature deaths with the same exposed level used in SCHIF method, and the comparison of results indicated that IER had made a much lower estimation with less annual amounts around 0.15-0.5 million premature deaths within 2013-2015.

Using MAIAC AOD to verify the PM2.5 spatial patterns of a land use regression model

Accurate spatial information of PM_2.5 is critical for air pollution control and epidemiological studies. Land use regression (LUR) models have been widely used for predicting spatial distribution of ground PM_2.5. However, the predicted PM_2.5 spatial patterns of a LUR model has not been adequately examined due to limited ground observations. The increasing aerosol optical depth (AOD) products might be an approximation of spatially continuous observation across large areas. This study established the relationship between seasonal 1 km × 1 km MAIAC AOD and observed ground PM_2.5 in Beijing, and then seasonal PM_2.5 maps were predicted based on AOD. Seasonal LUR models were also developed, and both the AOD and LUR models were validated by hold-out monitoring sites. Finally, the spatial patterns of LUR models were comprehensively verified by the above AOD PM_2.5 maps. The results showed that AOD alone could be used directly to predict the spatial distribution of ground PM_2.5 concentration at seasonal level (R² ≥ 0.53 in model fitting and testing), which was comparable with the capability of LUR models (R² ≥ 0.81 in model fitting and testing). PM_2.5 maps derived from the two methods showed similar spatial trend and coordinated variations near traffic roads. Large discrepancies could be observed at urban-rural transition areas where land use characters varied quickly. Variable and buffer size selection was critical for LUR model as they dominated the spatial patterns of predicted PM_2.5. Incorporating AOD into LUR model could improve model performance in spring season and provide more reliable results during testing.