Daily ambient NO2 concentration predictions using satellite ozone monitoring instrument NO2 data and land use regression

Inequalities in urban air pollution in sub-Saharan Africa: an empirical modeling of ambient NO and NO2 concentrations in Accra, Ghana

Road traffic has become the leading source of air pollution in fast-growing sub-Saharan African cities. Yet, there is a dearth of robust city-wide data for understanding space-time variations and inequalities in combustion related emissions and exposures. We combined nitrogen dioxide (NO2) and nitric oxide (NO) measurement data from 134 locations in the Greater Accra Metropolitan Area (GAMA), with geographical, meteorological, and population factors in spatio-temporal mixed effects models to predict NO2 and NO concentrations at fine spatial (50 m) and temporal (weekly) resolution over the entire GAMA. Model performance was evaluated with 10-fold cross-validation (CV), and predictions were summarized as annual and seasonal (dusty [Harmattan] and rainy [non-Harmattan]) mean concentrations. The predictions were used to examine population distributions of, and socioeconomic inequalities in, exposure at the census enumeration area (EA) level. The models explained 88% and 79% of the spatiotemporal variability in NO2 and NO concentrations, respectively. The mean predicted annual, non-Harmattan and Harmattan NO2 levels were 37 (range: 1-189), 28 (range: 1-170) and 50 (range: 1-195) µg m-3, respectively. Unlike NO2, NO concentrations were highest in the non-Harmattan season (41 [range: 31-521] µg m-3). Road traffic was the dominant factor for both pollutants, but NO2 had higher spatial heterogeneity than NO. For both pollutants, the levels were substantially higher in the city core, where the entire population (100%) was exposed to annual NO2 levels exceeding the World Health Organization (WHO) guideline of 10 µg m-3. Significant disparities in NO2 concentrations existed across socioeconomic gradients, with residents in the poorest communities exposed to levels about 15 µg m-3 higher compared with the wealthiest (p < 0.001). The results showed the important role of road traffic emissions in air pollution concentrations in the GAMA, which has major implications for the health of the city's poorest residents. These data could support climate and health impact assessments as well as policy evaluations in the city.

Analysis of variation characteristics, transport paths, and influencing factors of atmospheric NO2 pollution in Western Europe

Climate change and air pollution are one of the global environmental problems. It is significant to grasp the air pollution situation of Western Europe in recent 10 years for its or the global pollution control. Based on the OMI tropospheric nitrogen dioxide (NO2) column density data, the spatial and temporal distribution characteristics, variation trend, transmission path, and influencing factors of NO2 in 15 countries in Western Europe from 2011 to 2022 are discussed in this paper. Meanwhile, the annual average spatial and temporal distribution in 2023 is predicted by the random forest (RF) model. The results showed that (1) the 12-year spatial distribution map showed an increasing trend from southwest to northeast, with the border area of the Netherlands and Germany and Milan as two high-value areas, and the overall trend over time was that the high-concentration area gradually shrank, the low-concentration area gradually expanded, and the annual average concentration gradually decreased. (2) The inter-month trend presents a "U" shape, with the mean NO2 pollution ranking in winter > autumn > spring > summer. (3) Natural factors are one of the reasons affecting NO2; for instance, NO2 pollution has a strong positive correlation with the lifted index, relative humidity, and wind speed and a moderately strong negative correlation with precipitable water and air temperature. (4) Exogenous atmospheric transport is another important factor affecting the change of NO2 pollution in Western Europe. The HYSPLIT model is used to analyze the backward trajectory of Milan, Italy, and Nijmegen, Netherlands, in the four seasons of 2022. Both are mainly influenced by westerly airflows, and therefore, the transport effect in the atmosphere brings air pollutants from westerly regions in the atmosphere.

An assessment of NO2 atmospheric air pollution over three cities in South Africa during 2020 COVID-19 pandemic

To contain the spread of COVID-19 in 2020, several governments around the world imposed national lockdowns including that of South Africa. The purpose of this study was to investigate and give an overview of nitrogen dioxide column levels during the year 2020 over three South African cities (Johannesburg, Durban and Cape Town) using AURA OMI derived measurements, the HYSPLIT model, complemented with NCEP/NCAR reanalysis data. Our findings were that in 2020, all the cities recorded their daily maximum mean NO₂ column levels during the winter season at 14.1 × 10¹⁵ molecules per cm², 3.1 × 10¹⁵ molecules per cm² and 1.7 × 10¹⁵ molecules per cm² for Johannesburg, Durban, and Cape Town respectively. Across all seasons, Cape Town recorded the lowest seasonal mean at 0.6 × 10¹⁵ molecules per cm² (summer 2020) while the highest seasonal mean was recorded over Johannesburg at 9 × 10¹⁵ molecules cm² (winter 2020). Furthermore, an interannual comparison analysis indicated that during summer, there were increases of 6%, 1% and 30% for Johannesburg, Durban and Cape Town respectively. During winter, Johannesburg saw an increase of 19% while a 2% increase was recorded in Durban with Cape town recording a 16% decrease in NO₂ column levels. The study also recorded that Cape Town and Durban were mainly influenced by long-range transport air masses originating from the South Atlantic Ocean, South America, Antarctica and the Indian Ocean particularly during the summer and autumn seasons possibly leading to the formation of marine nitrate aerosols.

Using follicular fluid metabolomics to investigate the association between air pollution and oocyte quality

BACKGROUND AND AIM

Our objective was to use metabolomics in a toxicological-relevant target tissue to gain insight into the biological processes that may underlie the negative association between air pollution exposure and oocyte quality.

METHODS

Our study included 125 women undergoing in vitro fertilization at an academic fertility center in Massachusetts, US (2005-2015). A follicular fluid sample was collected during oocyte retrieval and untargeted metabolic profiling was conducted using liquid chromatography with ultra-high-resolution mass spectrometry and two chromatography columns (C18 and HILIC). Daily exposure to nitrogen dioxide (NO2), ozone, fine particulate matter, and black carbon was estimated at the women's residence using spatiotemporal models and averaged over the period of ovarian stimulation (2-weeks). Multivariable linear regression models were used to evaluate the associations between the air pollutants, number of mature oocytes, and metabolic feature intensities. A meet-in-the-middle approach was used to identify overlapping features and metabolic pathways.

RESULTS

Of the air pollutants, NO2 exposure had the largest number of overlapping metabolites (C18: 105; HILIC: 91) and biological pathways (C18: 3; HILIC: 6) with number of mature oocytes. Key pathways of overlap included vitamin D3 metabolism (both columns), bile acid biosynthesis (both columns), C21-steroid hormone metabolism (HILIC), androgen and estrogen metabolism (HILIC), vitamin A metabolism (HILIC), carnitine shuttle (HILIC), and prostaglandin formation (C18). Three overlapping metabolites were confirmed with level-1 or level-2 evidence. For example, hypoxanthine, a metabolite that protects against oxidant-induced cell injury, was positively associated with NO2 exposure and negatively associated with number of mature oocytes. Minimal overlap was observed between the other pollutants and the number of mature oocytes.

CONCLUSIONS

Higher exposure to NO2 during ovarian stimulation was associated with many metabolites and biologic pathways involved in endogenous vitamin metabolism, hormone synthesis, and oxidative stress that may mediate the observed associations with lower oocyte quality.

IoT and Satellite Sensor Data Integration for Assessment of Environmental Variables: A Case Study on NO2

This paper introduces a novel approach to increase the spatiotemporal resolution of an arbitrary environmental variable. This is achieved by utilizing machine learning algorithms to construct a satellite-like image at any given time moment, based on the measurements from IoT sensors. The target variables are calculated by an ensemble of regression models. The observed area is gridded, and partitioned into Voronoi cells based on the IoT sensors, whose measurements are available at the considered time. The pixels in each cell have a separate regression model, and take into account the measurements of the central and neighboring IoT sensors. The proposed approach was used to assess NO2 data, which were obtained from the Sentinel-5 Precursor satellite and IoT ground sensors. The approach was tested with three different machine learning algorithms: 1-nearest neighbor, linear regression and a feed-forward neural network. The highest accuracy yield was from the prediction models built with the feed-forward neural network, with an RMSE of 15.49 ×10-6 mol/m2.

Functional Kriging for Spatiotemporal Modeling of Nitrogen Dioxide in a Middle Eastern Megacity

Long-term hour-specific air pollution exposure estimates have rarely been of interest in epidemiological research. However, this can be relevant for studies that aim to estimate the residential exposure for the hours that subjects mostly spend time there, or for those hours that they may work in another location. Here, we developed a model by spatially predicting the long-term diurnal curves of nitrogen dioxide (NO2) in Tehran, Iran, one of the most polluted and populated megacities in the Middle East. We used the statistical framework of functional data analysis (FDA) including ordinary kriging for functional data (OKFD) and functional analysis of variance (fANOVA) for modeling. The long-term NO2 diurnal curves had two distinct maxima and minima. The absolute minimum value of the city average was 40.6 ppb (around 4:00 p.m.) and the absolute maximum value was 52.0 ppb (around 10:00 p.m.). The OKFD showed the concentrations, the diurnal maximum/minimum values, and their corresponding occurring times varied across the city. The fANOVA highlighted that the effect of population density on the NO2 concentrations is not constant and depends on time within the diurnal period. The provided estimation of long-term hour-specific maps can inform future epidemiological studies to use the long-term mean for specific hour(s) of the day. Moreover, the demonstrated FDA framework can be used as a set of flexible statistical methods.

Estimation of Surface NO2 Concentrations over Germany from TROPOMI Satellite Observations Using a Machine Learning Method

In this paper, we present the estimation of surface NO2 concentrations over Germany using a machine learning approach. TROPOMI satellite observations of tropospheric NO2 vertical column densities (VCDs) and several meteorological parameters are used to train the neural network model for the prediction of surface NO2 concentrations. The neural network model is validated against ground-based in situ air quality monitoring network measurements and regional chemical transport model (CTM) simulations. Neural network estimation of surface NO2 concentrations show good agreement with in situ monitor data with Pearson correlation coefficient (R) of 0.80. The results also show that the machine learning approach is performing better than regional CTM simulations in predicting surface NO2 concentrations. We also performed a sensitivity analysis for each input parameter of the neural network model. The validated neural network model is then used to estimate surface NO2 concentrations over Germany from 2018 to 2020. Estimated surface NO2 concentrations are used to investigate the spatio-temporal characteristics, such as seasonal and weekly variations of NO2 in Germany. The estimated surface NO2 concentrations provide comprehensive information of NO2 spatial distribution which is very useful for exposure estimation. We estimated the annual average NO2 exposure for 2018, 2019 and 2020 is 15.53, 15.24 and 13.27 µµg/m3, respectively. While the annual average NO2 concentration of 2018, 2019 and 2020 is only 12.79, 12.60 and 11.15 µµg/m3. In addition, we used the surface NO2 data set to investigate the impacts of the coronavirus disease 2019 (COVID-19) pandemic on ambient NO2 levels in Germany. In general, 10–30% lower surface NO2 concentrations are observed in 2020 compared to 2018 and 2019, indicating the significant impacts of a series of restriction measures to reduce the spread of the virus.

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.

Role of Meteorological Parameters in the Diurnal and Seasonal Variation of NO2 in a Romanian Urban Environment

The main purpose of this study was to investigate whether meteorological parameters (temperature, relative humidity, direct radiation) play an important role in modifying the NO₂ concentration in an urban environment. The diurnal and seasonal variation recorded at a NO₂ traffic station was analyzed, based on data collected in situ in a Romanian city, Braila (45.26° N, 27.95° E), during 2009-2014. The NO₂ atmospheric content close to the ground had, in general, a summer minimum and a late autumn/winter maximum for most years. Two diurnal peaks were observed, regardless of the season, which were more evident during cold months. Traffic is an important contributor to the NO₂ atmospheric pollution during daytime hours. The variability of in situ measurements of NO₂ concentration compared relatively well with space-based observations of the NO₂ vertical column by the Ozone Monitoring Instrument (OMI) satellite for most of the period under scrutiny. Data for daytime and nighttime (when the traffic is reduced) were analyzed separately, in the attempt to isolate meteorological effects. Meteorological parameters are not fully independent and we used partial correlation analysis to check whether the relationships with one parameter may be induced by another. The correlation between NO₂ and temperature was not coherent. Relative humidity and solar radiation seemed to play a role in shaping the NO₂ concentration, regardless of the time of day, and these relationships were only partially interconnected.

Assessing NO2 Concentration and Model Uncertainty with High Spatiotemporal Resolution across the Contiguous United States Using Ensemble Model Averaging

NO₂ is a combustion byproduct that has been associated with multiple adverse health outcomes. To assess NO₂ levels with high accuracy, we propose the use of an ensemble model to integrate multiple machine learning algorithms, including neural network, random forest, and gradient boosting, with a variety of predictor variables, including chemical transport models. This NO₂ model covers the entire contiguous U.S. with daily predictions on 1-km-level grid cells from 2000 to 2016. The ensemble produced a cross-validated R² of 0.788 overall, a spatial R² of 0.844, and a temporal R² of 0.729. The relationship between daily monitored and predicted NO₂ is almost linear. We also estimated the associated monthly uncertainty level for the predictions and address-specific NO₂ levels. This NO₂ estimation has a very high spatiotemporal resolution and allows the examination of the health effects of NO₂ in unmonitored areas. We found the highest NO₂ levels along highways and in cities. We also observed that nationwide NO₂ levels declined in early years and stagnated after 2007, in contrast to the trend at monitoring sites in urban areas, where the decline continued. Our research indicates that the integration of different predictor variables and fitting algorithms can achieve an improved air pollution modeling framework.

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.

Supplemental Folate and the Relationship Between Traffic-Related Air Pollution and Livebirth Among Women Undergoing Assisted Reproduction

Traffic-related air pollution has been linked to higher risks of infertility and miscarriage. We evaluated whether folate intake modified the relationship between air pollution and livebirth among women using assisted reproductive technology (ART). Our study included 304 women (513 cycles) presenting to a fertility center in Boston, Massachusetts (2005-2015). Diet and supplements were assessed by food frequency questionnaire. Spatiotemporal models estimated residence-based daily nitrogen dioxide (NO2), ozone, fine particulate, and black carbon concentrations in the 3 months before ART. We used generalized linear mixed models with interaction terms to evaluate whether the associations between air pollutants and livebirth were modified by folate intake, adjusting for age, body mass index, race, smoking, education, infertility diagnosis, and ART cycle year. Supplemental folate intake significantly modified the association of NO2 exposure and livebirth (P = 0.01). Among women with supplemental folate intakes of <800 μg/day, the odds of livebirth were 24% (95% confidence interval: 2, 42) lower for every 20-parts-per-billion increase in NO2 exposure. There was no association among women with intakes of ≥800 μg/day. There was no effect modification of folate on the associations between other air pollutants and livebirth. High supplemental folate intake might protect against the adverse reproductive consequences of traffic-related air pollution.

Time-Varying Exposure to Air Pollution and Outcomes of in Vitro Fertilization among Couples from a Fertility Clinic

BACKGROUND

A few studies suggest that air pollution may decrease fertility, but prospective studies and examinations of windows of susceptibility remain unclear.

OBJECTIVE

We aimed to examine the association between time-varying exposure to nitrogen dioxide ([Formula: see text]), ozone ([Formula: see text]), fine particulate matter [Formula: see text] ([Formula: see text]), and black carbon (BC) on in vitro fertilization (IVF) outcomes.

METHODS

We included 345 women (522 IVF cycles) for the [Formula: see text], [Formula: see text], and [Formula: see text] analyses and 339 women (512 IVF cycles) for the BC analysis enrolled in a prospective cohort at a Boston fertility center (2004–2015). We used validated spatiotemporal models to estimate daily residential exposure to [Formula: see text], [Formula: see text], [Formula: see text], and BC. Multivariable discrete time Cox proportional hazards models with four periods [ovarian stimulation (OS), oocyte retrieval to embryo transfer (ET), ET to implantation, implantation to live birth] estimated odds ratios (OR) and 95% confidence intervals (CI) of failing at IVF. Time-dependent interactions were used to identify vulnerable periods.

RESULTS

An interquartile range (IQR) increase in [Formula: see text], [Formula: see text], and BC throughout the IVF cycle was associated with an elevated odds of failing at IVF prior to live birth ([Formula: see text], 95% CI: 0.95, 1.23 for [Formula: see text]; [Formula: see text], 95% CI: 0.88, 1.28 for [Formula: see text]; and [Formula: see text], 95% CI: 0.96, 1.41 for BC). This relationship significantly varied across the IVF cycle such that the association with higher exposure to air pollution during OS was strongest for early IVF failures. An IQR increase in [Formula: see text], [Formula: see text], and BC exposure during OS was associated with 1.42 (95% CI: 1.20, 1.69), 1.26 (95% CI: 0.96, 1.67), and 1.23 (95% CI: 0.96, 1.59) times the odds of failing prior to oocyte retrieval, and 1.32 (95% CI: 1.13, 1.54), 1.27 (95% CI: 0.98, 1.65), and 1.32 (95% CI: 1.10, 1.59) times the odds of failing prior to ET.

CONCLUSION

Increased exposure to traffic-related pollutants was associated with higher odds of early IVF failure. https://doi.org/10.1289/EHP4601.

Evaluation of China's Environmental Pressures Based on Satellite NO2 Observation and the Extended STIRPAT Model

China’s rapid urbanization and industrialization have affected the spatiotemporal patterns of nitrogen dioxide (NO₂) pollution, which has led to greater environmental pressures. In order to mitigate the environmental pressures caused by NO₂ pollution, it is of vital importance to investigate the influencing factors. We first obtained data for NO₂ pollution at the city level using satellite observation techniques and analyzed its spatial distribution. Next, we introduced a theoretical framework, an extended stochastic impacts by regression on population, affluence, and technology (STIRPAT) model, to quantify the relationship between NO₂ pollution and its contributing natural and socio-economic factors. The results are as follows. Cities with high NO₂ pollution are mainly concentrated in the North China Plain. On the contrary, southwestern cities are characterized by low NO₂ pollution. In addition, we find that population, per capita gross domestic product, the share of the secondary industry, ambient air pressures, total nighttime light data, and urban road area have a positive impact on NO₂ pollution. In contrast, increases in the normalized difference vegetation index (NDVI), relative humidity, temperature, and wind speed may reduce NO₂ pollution. These empirical results should help the government to effectively and efficiently implement further emission reductions and energy saving policies in Chinese cities in a bid to mitigate the environmental pressures.

Satellite-Based Estimates of Daily NO2 Exposure in China Using Hybrid Random Forest and Spatiotemporal Kriging Model

A novel model named random-forest-spatiotemporal-kriging (RF-STK) was developed to estimate the daily ambient NO₂ concentrations across China during 2013-2016 based on the satellite retrievals and geographic covariates. The RF-STK model showed good prediction performance, with cross-validation R² = 0.62 (RMSE = 13.3 μg/m³) for daily and R² = 0.73 (RMSE = 6.5 μg/m³) for spatial predictions. The nationwide population-weighted multiyear average of NO₂ was predicted to be 30.9 ± 11.7 μg/m³ (mean ± standard deviation), with a slowly but significantly decreasing trend at a rate of -0.88 ± 0.38 μg/m³/year. Among the main economic zones of China, the Pearl River Delta showed the fastest decreasing rate of -1.37 μg/m³/year, while the Beijing-Tianjin Metro did not show a temporal trend ( P = 0.32). The population-weighted NO₂ was predicted to be the highest in North China (40.3 ± 10.3 μg/m³) and lowest in Southwest China (24.9 ± 9.4 μg/m³). Approximately 25% of the population lived in nonattainment areas with annual-average NO₂ > 40 μg/m³. A piecewise linear function with an abrupt point around 100 people/km² characterized the relationship between the population density and the NO₂, indicating a threshold of aggravated NO₂ pollution due to urbanization. Leveraging the ground-level NO₂ observations, this study fills the gap of statistically modeling nationwide NO₂ in China, and provides essential data for epidemiological research and air quality management.

Temporal trends in air pollution exposure inequality in Massachusetts

Mounting evidence over the past several decades has demonstrated inequitable distribution of pollutants of ambient origin between sociodemographic groups in the United States. Most environmental inequality studies to date are cross-sectional and used proximity-based methods rather than modeled air pollution concentrations, limiting the ability to examine trends over time or the factors that drive exposure inequalities. In this paper, we use 1km² modeled PM_2.5 and NO₂ concentrations in Massachusetts over an 8-year period and Census demographic data to quantify inequality between sociodemographic groups and to develop a more nuanced understanding of the drivers and trends in longitudinal air pollution inequality. Annual-average population-weighted PM_2.5 and NO₂ concentrations were highest for urban non-Hispanic black populations (11.8µg/m³ in 2003 and 8.4µg/m³ in 2010, vs. 11.3µg/m³ and 8.1µg/m³ for urban non-Hispanic whites) and urban Hispanic populations (15.9 ppb in 2005 and 13.0 ppb in 2010, vs. 13.0 ppb and 10.2 ppb for urban non-Hispanic whites), respectively. While population groups experienced similar absolute decreases in exposure over time, disparities in population-weighted concentrations increased over time when quantified by the Atkinson Index, a relative inequality measure. Exposure inequalities were approximately one order of magnitude greater for NO₂ compared to PM_2.5, were more pronounced in urban compared to rural geographies, and between racial/ethnic groups compared to income and educational attainment groups. Our results also revealed similar longitudinal PM_2.5 and NO₂ inequality trends using Census 2000 and Census 2010 data, indicating that spatio-temporal shifts in air pollution may best explain observed trends in inequality. These findings enhance our understanding of factors that contribute to persistent inequalities and underscore the importance of targeted exposure reduction strategies aimed at vulnerable populations and neighborhoods.

Air pollution, epigenetics, and asthma

Exposure to traffic-related air pollution (TRAP) has been implicated in asthma development, persistence, and exacerbation. This exposure is highly significant as large segments of the global population resides in zones that are most impacted by TRAP and schools are often located in high TRAP exposure areas. Recent findings shed new light on the epigenetic mechanisms by which exposure to traffic pollution may contribute to the development and persistence of asthma. In order to delineate TRAP induced effects on the epigenome, utilization of newly available innovative methods to assess and quantify traffic pollution will be needed to accurately quantify exposure. This review will summarize the most recent findings in each of these areas. Although there is considerable evidence that TRAP plays a role in asthma, heterogeneity in both the definitions of TRAP exposure and asthma outcomes has led to confusion in the field. Novel information regarding molecular characterization of asthma phenotypes, TRAP exposure assessment methods, and epigenetics are revolutionizing the field. Application of these new findings will accelerate the field and the development of new strategies for interventions to combat TRAP-induced asthma.

Enhancing the Applicability of Satellite Remote Sensing for PM2.5 Estimation Using MODIS Deep Blue AOD and Land Use Regression in California, United States

We estimated daily ground-level PM2.5 concentrations combining Collection 6 deep blue (DB) Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) data (10 km resolution) with land use regression in California, United States, for the period 2006-2012. The Collection 6 DB method for AOD provided more reliable data retrievals over California's bright surface areas than previous data sets. Our DB AOD and PM2.5 data suggested that the PM2.5 predictability could be enhanced by temporally varying PM2.5 and AOD relations at least at a seasonal scale. In this study, we used a mixed effects model that allowed daily variations in DB AOD-PM2.5 relations. Because DB AOD might less effectively represent local source emissions compared to regional ones, we added geographic information system (GIS) predictors into the mixed effects model to further explain PM2.5 concentrations influenced by local sources. A cross validation (CV) mixed effects model revealed reasonably high predictive power for PM2.5 concentrations with R(2) = 0.66. The relations between DB AOD and PM2.5 considerably varied by day, and seasonally varying effects of GIS predictors on PM2.5 suggest season-specific source emissions and atmospheric conditions. This study indicates that DB AOD in combination with land use regression can be particularly useful to generate spatially resolved PM2.5 estimates. This may reduce exposure errors for health effect studies in California. We expect that more detailed PM2.5 concentration patterns can help air quality management plan to meet air quality standards more effectively.

Air pollution and allergic diseases

PURPOSE OF REVIEW

Exposure to traffic-related air pollutants (TRAPs) has been implicated in asthma development, persistence, and exacerbation. This exposure is highly significant because increasingly large segments of the population worldwide reside in zones that have high levels of TRAP, including children, as schools are often located in high traffic pollution exposure areas.

RECENT FINDINGS

Recent findings include epidemiologic and mechanistic studies that shed new light on the impact of traffic pollution on allergic diseases and the biology underlying this impact. In addition, new innovative methods to assess and quantify traffic pollution have been developed to assess exposure and identify vulnerable populations and individuals.

SUMMARY

This review will summarize the most recent findings in each of these areas. These findings will have a substantial impact on clinical practice and research by the development of novel methods to quantify exposure and identify at-risk individuals, as well as mechanistic studies that identify new targets for intervention for individuals most adversely affected by TRAP exposure.

National Spatiotemporal Exposure Surface for NO2: Monthly Scaling of a Satellite-Derived Land-Use Regression, 2000-2010

Land-use regression (LUR) is widely used for estimating within-urban variability in air pollution. While LUR has recently been extended to national and continental scales, these models are typically for long-term averages. Here we present NO2 surfaces for the continental United States with excellent spatial resolution (∼100 m) and monthly average concentrations for one decade. We investigate multiple potential data sources (e.g., satellite column and surface estimates, high- and standard-resolution satellite data, and a mechanistic model [WRF-Chem]), approaches to model building (e.g., one model for the whole country versus having separate models for urban and rural areas, monthly LURs versus temporal scaling of a spatial LUR), and spatial interpolation methods for temporal scaling factors (e.g., kriging versus inverse distance weighted). Our core approach uses NO2 measurements from U.S. EPA monitors (2000-2010) to build a spatial LUR and to calculate spatially varying temporal scaling factors. The model captures 82% of the spatial and 76% of the temporal variability (population-weighted average) of monthly mean NO2 concentrations from U.S. EPA monitors with low average bias (21%) and error (2.4 ppb). Model performance in absolute terms is similar near versus far from monitors, and in urban, suburban, and rural locations (mean absolute error 2-3 ppb); since low-density locations generally experience lower concentrations, model performance in relative terms is better near monitors than far from monitors (mean bias 3% versus 40%) and is better for urban and suburban locations (1-6%) than for rural locations (78%, reflecting the relatively clean conditions in many rural areas). During 2000-2010, population-weighted mean NO2 exposure decreased 42% (1.0 ppb [∼5.2%] per year), from 23.2 ppb (year 2000) to 13.5 ppb (year 2010). We apply our approach to all U.S. Census blocks in the contiguous United States to provide 132 months of publicly available, high-resolution NO2 concentration estimates.