Nitrogen dioxide prediction in Southern California using land use regression modeling: potential for environmental health analyses

Leveraging low-cost sensors to predict nitrogen dioxide for epidemiologic exposure assessment

BACKGROUND

Statistical models of air pollution enable intra-urban characterization of pollutant concentrations, benefiting exposure assessment for environmental epidemiology. The new generation of low-cost sensors facilitate the deployment of dense monitoring networks and can potentially be used to improve intra-urban models of air pollution.

OBJECTIVE

Develop and evaluate a spatiotemporal model for nitrogen dioxide (NO2) in the Puget Sound region of WA, USA for the Adult Changes in Thought Air Pollution (ACT-AP) study and assess the contribution of low-cost sensor data to the model's performance through cross-validation.

METHODS

We developed a spatiotemporal NO2 model for the study region incorporating data from 11 agency locations, 364 supplementary monitoring locations, and 117 low-cost sensor (LCS) locations for the 1996-2020 time period. Model features included long-term time trends and dimension-reduced land use regression. We evaluated the contribution of LCS network data by comparing models fit with and without sensor data using cross-validated (CV) summary performance statistics.

RESULTS

The best performing model had one time trend and geographic covariates summarized into three partial least squares components. The model, fit with LCS data, performed as well as other recent studies (agency cross-validation: CV- root mean square error (RMSE) = 2.5 ppb NO2; CV- coefficient of determination (R 2 ) = 0.85). Predictions of NO2 concentrations developed with LCS were higher at residential locations compared to a model without LCS, especially in recent years. While LCS did not provide a strong performance gain at agency sites (CV-RMSE = 2.8 ppb NO2; CV-R 2  = 0.82 without LCS), at residential locations, the improvement was substantial, with RMSE = 3.8 ppb NO2 andR 2  = 0.08 (without LCS), compared to CV-RMSE = 2.8 ppb NO2 and CV-R 2  = 0.51 (with LCS).

IMPACT

We developed a spatiotemporal model for nitrogen dioxide (NO2) pollution in Washington's Puget Sound region for epidemiologic exposure assessment for the Adult Changes in Thought Air Pollution study. We examined the impact of including low-cost sensor data in the NO2 model and found the additional spatial information the sensors provided predicted NO2 concentrations that were higher than without low-cost sensors, particularly in recent years. We did not observe a clear, substantial improvement in cross-validation performance over a similar model fit without low-cost sensor data; however, the prediction improvement with low-cost sensors at residential locations was substantial. The performance gains from low-cost sensors may have been attenuated due to spatial information provided by other supplementary monitoring data.

Development of Spatio-Temporal Land Use Regression Models for Fine Particulate Matter and Wood-Burning Tracers in Temuco, Chile

Biomass burning is common in much of the world, and in some areas, residential wood-burning has increased. However, air pollution resulting from biomass burning is an important public health problem. A sampling campaign was carried out between May 2017 and July 2018 in over 64 sites in four sessions, to develop a spatio-temporal land use regression (LUR) model for fine particulate matter (PM) and wood-burning tracers levoglucosan and soluble potassium (Ksol) in a city heavily impacted by wood-burning. The mean (sd) was 46.5 (37.4) μg m-3 for PM2.5, 0.607 (0.538) μg m-3 for levoglucosan, and 0.635 (0.489) μg m-3 for Ksol. LUR models for PM2.5, levoglucosan, and Ksol had a satisfactory performance (LOSOCV R2), explaining 88.8%, 87.4%, and 87.3% of the total variance, respectively. All models included sociodemographic predictors consistent with the pattern of use of wood-burning in homes. The models were applied to predict concentrations surfaces and to estimate exposures for an epidemiological study.

Mitigation strategies for controlling urban particulate pollution from traffic congestion: Road expansion and road public transport

The mismatch between urban transportation demands and local transportation supplies in municipal regions may cause serious traffic congestion and high vehicle exhaust emissions (VEEs). Although it has been found that road expansion and the provision of road public transport can mitigate traffic congestion and VEEs, the effectiveness of these public strategies in alleviating traffic congestion-related VEEs has been empirically compared in few studies. With the use of a panel dataset for 260 prefecture-level cities in China from 2013 to 2018, we fill this gap via empirically examining the effects of several popular strategies that promote the efficiency of urban road transportation, including road expansion and road public transport provision, on urban air pollution of fine particulate matter (PM2.5). Then we further investigate metrics measured by passenger and freight volumes based on local urban road coverage that can suitably reflect the mismatch degree between local transportation demands and supplies. The empirical results indicated a positive and statistically significant correlation between urban PM2.5 levels and road expansion but not between PM2.5 levels and road public transport, suggesting that road expansion may unintentionally increase the transportation volume and thus local PM2.5 levels. Importantly, we found that the passenger and freight volumes per unit road length are negatively and significantly correlated with the local particulate pollution concentration across different model specifications, while the passenger and freight volumes of road transportation do not directly affect local PM2.5 levels. We also employed a series of robustness tests to overcome the concerns associated with measurement errors, omitted variable biases, model misspecifications and endogeneity, and consistent results were obtained. These results indicated that when facing limited public budgets, the passenger and freight volumes per unit road length should be considered to measure the effectiveness of road infrastructure investments in alleviating local particulate pollution caused by traffic congestion.

Green Heart Louisville: intra-urban, hyperlocal land-use regression modeling of nitrogen oxides and ozone

Exposure to urban air pollution is linked to increased mortality from cardiopulmonary causes. Urban areas juxtapose large numbers of residences and workplaces with near-road environments, exacerbating traffic-related air pollution (TRAP) exposure. TRAP is the primary source of variability in intraurban air quality, but continuous regulatory monitoring stations lack the spatial resolution to detect fine-scale pollutant patterns that recent studies using long-term, resource-intensive mobile measurements have established as persistent and associated with higher risk of cardiovascular events. This work evaluates a low-cost, fixed-site approach to characterizinglong-term, hyperlocal exposure to oxides of nitrogen (including NO ₂ , a common surrogate for TRAP) as part of Green Heart Louisville, a prospective cohort study examining linkages between urban vegetation, local air quality, and cardiovascular health. We used a fixed 60-site network of Ogawa passive samplers in a 12 km ² section of Louisville, KY, to measure two-week integrated NO ₂ , NO _x (NO + NO ₂ ), and O ₃ mixing ratios nominally every two months between May 2018-March 2021. Seasonal NO _x averages were 2.5-fold higher during winter than in summer, and annual average NO (calculated by difference in NO _x and NO ₂ ) and NO ₂ ranged from 4-21 ppb and 5-12 ppb, respectively. NO increased 3-to-5-fold within 150 m of highways or major arterial roads and 2-to-3-fold near parking lots. While both NO and NO ₂ were elevated in near-road environments, the corresponding O ₃ was depressed, consistent with titration by NO. We developed land-use regression models for annual average NO, NO ₂ , and NO _x using parameters of proximity (distance to nearest road type, restaurant, traffic signal), cumulative occurrence (length of roads, number of restaurants and traffic lights, all in buffers of up to 500 m in 50-m increments), and greenness (normalized difference vegetative index (NDVI)). Adjusted spatial variability explained by the models were 70% (p<0.05), 67% (p<0.05), and 75% (p<0.01) for NO, NO ₂ , and NO _x , respectively. Common predictors were distances to the nearest restaurant and road as well as total length of roads within 350 m. Only one greenness metric was significant: mean NDVI within 50 m was negatively associated (p=0.02) with NO ₂ . We plan to use these hyperlocal models to estimate residential-level exposures of the clinical study participants.

Land-Use Regression Modeling to Estimate NO2 and VOC Concentrations in Pohang City, South Korea

Land-use regression (LUR) has emerged as a promising technique for air pollution modeling to obtain the spatial distribution of air pollutants for epidemiological studies. LUR uses traffic, geographic, and monitoring data to develop regression models and then predict the concentration of air pollutants in the same area. To identify the spatial distribution of nitrogen dioxide (NO2), benzene, toluene, and m-p-xylene, we developed LUR models in Pohang City, one of the largest industrialized areas in Korea. Passive samplings were conducted during two 2-week integrated sampling periods in September 2010 and March 2011, at 50 sampling locations. For LUR model development, predictor variables were calculated based on land use, road lengths, point sources, satellite remote sensing, and population density. The averaged mean concentrations of NO2, benzene, toluene, and m-p-xylene were 28.4 µg/m3, 2.40 µg/m3, 15.36 µg/m3, and 0.21 µg/m3, respectively. In terms of model-based R2 values, the model for NO2 included four independent variables, showing R2 = 0.65. While the benzene and m-p-xylene models showed the same R2 values (0.43), toluene showed a lower R2 value (0.35). We estimated long-term concentrations of NO2 and VOCs at 167,057 addresses in Pohang. Our study could hold particular promise in an epidemiological setting having significant health effects associated with small area variations and encourage the extended study using LUR modeling in Asia.

Land Use Quantile Regression Modeling of Fine Particulate Matter in Australia

Small data samples are still a critical challenge for spatial predictions. Land use regression (LUR) is a widely used model for spatial predictions with observations at a limited number of locations. Studies have demonstrated that LUR models can overcome the limitation exhibited by other spatial prediction models which usually require greater spatial densities of observations. However, the prediction accuracy and robustness of LUR models still need to be improved due to the linear regression within the LUR model. To improve LUR models, this study develops a land use quantile regression (LUQR) model for more accurate spatial predictions for small data samples. The LUQR is an integration of the LUR and quantile regression, which both have advantages in predictions with a small data set of samples. In this study, the LUQR model is applied in predicting spatial distributions of annual mean PM2.5concentrations across the Greater Sydney Region, New South Wales, Australia, with observations at 19 valid monitoring stations in 2020. Cross validation shows that the goodness-of-fit can be improved by 25.6–32.1% by LUQR models when compared with LUR, and prediction root mean squared error (RMSE) and mean absolute error (MAE) can be reduced by 10.6–13.4% and 19.4–24.7% by LUQR models, respectively. This study also indicates that LUQR is a more robust model for the spatial prediction with small data samples than LUR. Thus, LUQR has great potentials to be widely applied in spatial issues with a limited number of observations.

Relationship between Land-Use Type and Daily Concentration and Variability of PM10 in Metropolitan Cities: Evidence from South Korea

Since urban areas with high air pollution are known to have higher mortality rates compared to areas with less air pollution, accurately understanding and predicting the distribution of particulate matter (PM) in cities is important for urban planning policies that seek to emphasize the health of citizens. Therefore, this study aims to investigate the relationship between PM and land use in metropolitan cities in South Korea using the land-use regression model. We use daily data from the air quality monitoring stations (AQMS) in seven cities in South Korea for the year 2018. For analysis, K-means clustering is employed to identify the land-use pattern surrounding the AQMSs and two log-lin regression models are used to investigate the effects of each land-use type on PM. The findings show a statistically significant difference in PM concentration and variability in the business, commercial, industrial, mixed, and high-density residential areas compared to parks and green areas, and that PM concentration and variability were less in mixed areas than in single land use, thus verifying the effectiveness of a mixed land-use planning strategy. Moreover, microclimatic, seasonal, and regional factors affect PM concentration and variability. Finally, to minimize exposure to PM, various policies such as mixed land use need to be established and implemented differently, depending on the season and time.

Ambient nitrogen dioxide and years of life lost from chronic obstructive pulmonary disease in the elderly: A multicity study in China

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide, and nitrogen dioxide (NO₂) is a potential environmental risk factor for COPD. However, association between ambient NO₂ and COPD risk remains underrecognized, especially in the elderly. This study aimed to explore association between NO₂ and years of life lost (YLL) from COPD in the elderly from 2013 to 2017 in 37 major cities in China.

METHODS

Ambient NO₂ data and COPD morality information were obtained from the National Urban Air Quality Real-time Publishing Platform and the Chinese Centers for Disease Control and Prevention, respectively. City-specific relative changes in YLL were estimated by generalized additive models, and meta-analysis was used to combine city-specific results. Potential modifications were evaluated. Economic loss due to excess YLL from COPD associated with ambient NO₂ was evaluated.

RESULTS

An increase of 10 μg/m³ in NO₂ for 2-day moving average led to 0.94% (95% CI: 0.56%, 1.31%) relative increase in COPD YLL. The associations were significantly higher in South than North China. Higher estimated effects were found in the warm than the cool season in the southern region. The relevant economic loss accounted for 0.04% (95% CI: 0.02%, 0.05%) of the gross domestic product (GDP) in China during the same period.

CONCLUSIONS

The findings provide evidence on the impact of short-term NO₂ exposure on COPD YLL in the elderly, which indicated more stringent control of NO₂ pollution and highlighted the need to protect the elderly during the warm season in South China.

The effects of infrastructure development and carbon emissions on economic growth

Infrastructure is a basic physical structure that is essential for the facilitation of society in terms of the provision of the basic framework that is required for the economy's output. The transport infrastructure is one of the most promising tools for generating economic growth and development because of its constructive impact on multidimensional aspects of society. The carbon emissions emanating from different sectors are used to capture the environmental quality in Pakistan and its association with road infrastructure of the country. Transportation is the main source of carbon emissions (GHGs), and road transport is the biggest emitter that accounted for more than 70% of emissions from GHGs in 2014 in Pakistan. We have used fully modified ordinary least square (FMOLS) and dynamic ordinary least square (DOLS) approaches to analyze the role of carbon emissions (aggregate and disaggregate forms) along with road infrastructure on economic growth for the period of 1972-2017. Moreover, before the main analysis, the co-integrating relationships are also analyzed in this study. Our findings pronounce that there is a positive observable relationship between road infrastructure and economic growth. However, the impact of carbon emissions (irrespective of aggregate or disaggregate forms) is negative on economic growth. The findings suggest that the improvements in economic growth can be achieved through road infrastructure structure development but at cost of deterioration of environmental quality (increase in carbon emissions). Although emissions diminish economic growth over time but on the other side, the economic incorporation increases through the investment in road infrastructure which is of assistance for the construction and manufacturing industry.

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

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

Urbanization and regional air pollution across South Asian developing countries - A nationwide land use regression for ambient PM2.5 assessment in Pakistan

Rapid economic growth, urban sprawl, and unplanned industrialization has increased socioeconomic statuses but also decreased air quality in South Asian developing countries. Therefore, severe increase in air pollution has been a threat of local population, regarding health statuses, livability and quality of life. It is necessary to estimate fine-scale spatiotemporal distribution of ambient PM_2.5 in a national context so that the environmental planners and government officials can use it for environmental protocol development and policy-making. In this study, a spatiotemporal land use regression (LUR) model is developed to refine global air quality data to the national-scale ambient PM_2.5 exposure in a high-density country in South Asia - Pakistan. Combining with transport network, patterns of land use, local meteorological conditions, geographic characteristics, landscape characteristics, and satellite-derived data, our resultant model explains 54.5% of the variation in ambient PM_2.5 concentration level. Furthermore, tree coverage and road transport are identified to be two influential factors of the national-scale spatial variation of PM_2.5 in Pakistan, which implied that urbanization might be the major cause of air pollution across the country. In conclusion, our resultant LUR model as well as the spatial map of ambient PM_2.5 concentration level can be used as a supporting tool for national health risk management and environmental planning, and could also contribute to the air quality management and pollution reduction actions of Pakistan.

Using universal kriging to improve neighborhood physical disorder measurement

Ordinary kriging, a spatial interpolation technique, is commonly used in social sciences to estimate neighborhood attributes such as physical disorder. Universal kriging, developed and used in physical sciences, extends ordinary kriging by supplementing the spatial model with additional covariates. We measured physical disorder on 1,826 sampled block faces across 4 US cities (New York, Philadelphia, Detroit, and San Jose) using Google Street View imagery. We then compared leave-one-out cross-validation accuracy between universal and ordinary kriging and used random subsamples of our observed data to explore whether universal kriging could provide equal measurement accuracy with less spatially dense samples. Universal kriging did not always improve accuracy. However, a measure of housing vacancy did improve estimation accuracy in Philadelphia and Detroit (7.9 and 6.8% lower root mean square error, respectively) and allowed for equivalent estimation accuracy with half the sampled points in Philadelphia. Universal kriging may improve neighborhood measurement.

Predicting Traffic-Related Air Pollution Using Feature Extraction from Built Environment Images

This study develops a set of algorithms to extract built environment features from Google aerial and street view images, reflecting the microcharacteristics of an urban location as well as the different functions of buildings. These features were used to train a Bayesian regularized artificial neural network (BRANN) model to predict near-road air quality based on measurements of ultrafine particles (UFPs) and black carbon (BC) in Toronto, Canada. The resulting models [adjusted R² of 75.87 and 79.10% for UFP and BC and root mean squared error (RMSE) of 21,800 part/cm³ and 1300 ng/m³ for UFP and BC] were compared with similar ANN models developed using the same predictors, but extracted from traditional geographic information system (GIS) databases [adjusted R² of 58.74 and 64.21% for UFP and BC and RMSE values of 23,000 part/cm³ and 1600 ng/m³ for UFP and BC]. The models based on feature extraction exhibited higher predictive power, thus highlighting the greater accuracy of the proposed methods compared to GIS layers that are solely based on aerial images. A comparison with other neural network approaches as well as with a traditional land-use regression model demonstrates the strength of the BRANN model for spatial interpolation of air quality.

Vehicle emissions and fertilizer impact the leaf chemistry of urban trees in Salt Lake Valley, UT

The urban nitrogen (N) and carbon (C) cycles are substantially influenced by human activity. Alterations to these cycles include increased inputs from fossil fuel combustion and fertilizer use. The leaf chemistry of urban trees can be used to distinguish between these different N and C sources. Here, we evaluated relationships between urban vegetation and different N and C sources in street and residential trees in the Salt Lake Valley, Utah. We tested three hypotheses: 1) unfertilized street trees on high traffic density roads will have higher leaf %N, more enriched δ¹⁵N and more depleted δ¹³C than unfertilized street trees on low traffic density roads; 2) trees in high income residential neighborhoods will have higher leaf %N, more depleted δ¹⁵N and more enriched δ¹³C than trees in lower income neighborhoods; and 3) unfertilized street trees will have lower leaf %N, more enriched δ¹⁵N and more depleted δ¹³C than fertilized residential trees. Leaf δ¹⁵N was more enriched near high traffic density roads for one study species. However, street tree δ¹⁵N and δ¹³C were largely influenced by vehicle emissions from primary and secondary roads within 1000 m radius rather than the immediately adjacent road. Leaf δ¹³C was correlated with neighborhood income, although this relationship may be the result of variations in irrigation practices rather than variations in C sources. Finally, unfertilized trees in downtown Salt Lake had lower leaf %N, more enriched δ¹⁵N and more depleted δ¹³C than fertilized trees. These results highlight that urban trees can serve as biomonitors of the environment. Moreover, they emphasize that roads can have large spatial footprints and that the leaf chemistry of urban vegetation may be influenced by the spatial patterns in roads and road densities at the landscape scale.

A land use regression model of nitrogen dioxide and fine particulate matter in a complex urban core in Lanzhou, China

BACKGROUND

Land use regression (LUR) models have been widely used to estimate air pollution exposures at high spatial resolution. However, few LUR models were developed for rapidly developing urban cores, which have substantially higher densities of population and built-up areas than the surrounding areas within a city's administrative boundary. Further, few studies incorporated vertical variations of air pollution in exposure assessment, which might be important to estimate exposures for people living in high-rise buildings.

OBJECTIVE

A LUR model was developed for the urban core of Lanzhou, China, along with a model of vertical concentration gradients in high-rise buildings.

METHODS

In each of four seasons in 2016-2017, NO₂ was measured using Ogawa badges for 2 weeks at 75 ground-level sites. PM_2.5 was measured using DataRAM for shorter time intervals at a subset (N = 38) of the 75 sites. Vertical profile measurements were conducted on 9 stories at 2 high-rise buildings (N = 18), with one building facing traffic and another facing away from traffic. The average seasonal concentrations of NO₂ and PM_2.5 at ground level were regressed against spatial predictors, including elevation, population, road network, land cover, and land use. The vertical variations were investigated and linked to ground-level predictions with exponential models.

RESULTS

We developed robust LUR models at the ground level for estimated annual averages of NO₂ (R²: 0.71, adjusted R²: 0.67, and Leave-One-Out Cross Validation (LOOCV) R²: 0.64) and PM_2.5 (R²: 0.77, adjusted R²: of 0.73, and LOOCV R²: 0.67) in the urban core of Lanzhou, China. The LUR models for the estimated seasonal averages of NO₂ showed similar patterns. Vertical variation of NO₂ and PM_2.5 differed by windows orientation with respect to traffic, by season or by time of a day. Vertical variation functions incorporated the ground-level LUR predictions, in a form that could allow for exposure assessment in future epidemiological investigations.

CONCLUSIONS

Ground-level NO₂ and PM_2.5 showed substantial spatial variations, explained by traffic and land use patterns. Further, vertical variation of air pollution levels is significant under certain conditions, suggesting that exposure misclassification could occur with traditional LUR that ignores vertical variation. More studies are needed to fully characterize three-dimensional concentration patterns to accurately estimate air pollution exposures for residents in high-rise buildings, but our LUR models reinforce that concentration heterogeneity is not captured by the limited government monitors in the Lanzhou urban area.

Association between ambient air pollution and breast cancer risk: The multiethnic cohort study

Previous studies using different exposure methods to assess air pollution and breast cancer risk among primarily whites have been inconclusive. Air pollutant exposures of particulate matter and oxides of nitrogen were estimated by kriging (NO_x , NO₂ , PM₁₀ , PM_2.5 ), land use regression (LUR, NO_x , NO₂ ) and California Line Source Dispersion model (CALINE4, NO_x , PM_2.5 ) for 57,589 females from the Multiethnic Cohort, residing largely in Los Angeles County from recruitment (1993-1996) through 2010. Cox proportional hazards models were used to examine the associations between time-varying air pollution and breast cancer incidence adjusting for confounding factors. Stratified analyses were conducted by race/ethnicity and distance to major roads. Among all women, breast cancer risk was positively but not significantly associated with NO_x (per 50 parts per billion [ppb]) and NO₂ (per 20 ppb) determined by kriging and LUR and with PM_2.5 and PM₁₀ (per 10 μg/m³ ) determined by kriging. However, among women who lived within 500 m of major roads, significantly increased risks were observed with NO_x (hazard ratio [HR] = 1.35, 95% confidence interval [95% CI]: 1.02-1.79), NO₂ (HR = 1.44, 95% CI: 1.04-1.99), PM₁₀ (HR = 1.29, 95% CI: 1.07-1.55) and PM_2.5 (HR = 1.85, 95% CI: 1.15-2.99) determined by kriging and NO_x (HR = 1.21, 95% CI:1.01-1.45) and NO₂ (HR = 1.26, 95% CI: 1.00-1.59) determined by LUR. No overall associations were observed with exposures assessed by CALINE4. Subgroup analyses suggested stronger associations of NO_x and NO₂ among African Americans and Japanese Americans. Further studies of multiethnic populations to confirm the effects of air pollution, particularly near-roadway exposures, on the risk of breast cancer is warranted.

Determining broad scale associations between air pollutants and urban forestry: A novel multifaceted methodological approach

Global urbanisation has resulted in population densification, which is associated with increased air pollution, mainly from anthropogenic sources. One of the systems proposed to mitigate urban air pollution is urban forestry. This study quantified the spatial associations between concentrations of CO, NO₂, SO₂, and PM₁₀ and urban forestry, whilst correcting for anthropogenic sources and sinks, thus explicitly testing the hypothesis that urban forestry is spatially associated with reduced air pollution on a city scale. A Land Use Regression (LUR) model was constructed by combining air pollutant concentrations with environmental variables, such as land cover type and use, to develop predictive models for air pollutant concentrations. Traffic density and industrial air pollutant emissions were added to the model as covariables to permit testing of the main effects after correcting for these air pollutant sources. It was found that the concentrations of all air pollutants were negatively correlated with tree canopy cover and positively correlated with dwelling density, population density and traffic count. The LUR models enabled the establishment of a statistically significant spatial relationship between urban forestry and air pollution mitigation. These findings further demonstrate the spatial relationships between urban forestry and reduced air pollution on a city-wide scale, and could be of value in developing planning policies focused on urban greening.

Developing a geostatistical simulation method to inform the quantity and placement of new monitors for a follow-up air sampling campaign

Sampling campaign design is a crucial aspect of air pollution exposure studies. Selection of both monitor numbers and locations is important for maximizing measured information, while minimizing bias and costs. We developed a two-stage geostatistical-based method using pilot NO₂ samples from Lanzhou, China with the goal of improving sample design decision-making, including monitor numbers and spatial pattern. In the first step, we evaluate how additional monitors change prediction precision through minimized kriging variance. This was assessed in a Monte Carlo fashion by adding up to 50 new monitors to our existing sites with assigned concentrations based on conditionally simulated NO₂ surfaces. After identifying a number of additional sample sites, a second step evaluates their potential placement using a similar Monte Carlo scheme. Evaluations are based on prediction precision and accuracy. Costs are also considered in the analysis. It was determined that adding 28-locations to the existing Lanzhou NO₂ sampling campaign captured 73.5% of the total kriged variance improvement and resulted in predictions that were on average within 10.9 μg/m³ of measured values, while using 56% of the potential budget. Additional monitor sites improved kriging variance in a nonlinear fashion. This method development allows for informed sampling design by quantifying prediction improvement (accuracy and precision) against the costs of monitor deployment.

Comparison of land use regression models for NO2 based on routine and campaign monitoring data from an urban area of Japan

BACKGROUND

Typically, land use regression (LUR) models have been developed using campaign monitoring data rather than routine monitoring data. However, the latter have advantages such as low cost and long-term coverage. Based on the idea that LUR models representing regional differences in air pollution and regional road structures are optimal, the objective of this study was to evaluate the validity of LUR models for nitrogen dioxide (NO₂) based on routine and campaign monitoring data obtained from an urban area.

METHOD

We selected the city of Suita in Osaka (Japan). We built a model based on routine monitoring data obtained from all sites (routine-LUR-All), and a model based on campaign monitoring data (campaign-LUR) within the city. Models based on routine monitoring data obtained from background sites (routine-LUR-BS) and based on data obtained from roadside sites (routine-LUR-RS) were also built. The routine LUR models were based on monitoring networks across two prefectures (i.e., Osaka and Hyogo prefectures). We calculated the predictability of the each model. We then compared the predicted NO₂ concentrations from each model with measured annual average NO₂ concentrations from evaluation sites.

RESULTS

The routine-LUR-All and routine-LUR-BS models both predicted NO₂ concentrations well: adjusted R²=0.68 and 0.76, respectively, and root mean square error=3.4 and 2.1ppb, respectively. The predictions from the routine-LUR-All model were highly correlated with the measured NO₂ concentrations at evaluation sites. Although the predicted NO₂ concentrations from each model were correlated, the LUR models based on routine networks, and particularly those based on all monitoring sites, provided better visual representations of the local road conditions in the city.

CONCLUSION

The present study demonstrated that LUR models based on routine data could estimate local traffic-related air pollution in an urban area. The importance and usefulness of data from routine monitoring networks should be acknowledged.

Capturing the sensitivity of land-use regression models to short-term mobile monitoring campaigns using air pollution micro-sensors

The development of reliable measures of exposure to traffic-related air pollution is crucial for the evaluation of the health effects of transportation. Land-use regression (LUR) techniques have been widely used for the development of exposure surfaces, however these surfaces are often highly sensitive to the data collected. With the rise of inexpensive air pollution sensors paired with GPS devices, we witness the emergence of mobile data collection protocols. For the same urban area, can we achieve a 'universal' model irrespective of the number of locations and sampling visits? Can we trade the temporal representation of fixed-point sampling for a larger spatial extent afforded by mobile monitoring? This study highlights the challenges of short-term mobile sampling campaigns in terms of the resulting exposure surfaces. A mobile monitoring campaign was conducted in 2015 in Montreal; nitrogen dioxide (NO₂) levels at 1395 road segments were measured under repeated visits. We developed LUR models based on sub-segments, categorized in terms of the number of visits per road segment. We observe that LUR models were highly sensitive to the number of road segments and to the number of visits per road segment. The associated exposure surfaces were also highly dissimilar.

Mining Public Datasets for Modeling Intra-City PM2.5 Concentrations at a Fine Spatial Resolution

Air quality models are important for studying the impact of air pollutant on health conditions at a fine spatiotemporal scale. Existing work typically relies on area-specific, expert-selected attributes of pollution emissions (e,g., transportation) and dispersion (e.g., meteorology) for building the model for each combination of study areas, pollutant types, and spatiotemporal scales. In this paper, we present a data mining approach that utilizes publicly available OpenStreetMap (OSM) data to automatically generate an air quality model for the concentrations of fine particulate matter less than 2.5 μm in aerodynamic diameter at various temporal scales. Our experiment shows that our (domain-) expert-free model could generate accurate PM2.5 concentration predictions, which can be used to improve air quality models that traditionally rely on expert-selected input. Our approach also quantifies the impact on air quality from a variety of geographic features (i.e., how various types of geographic features such as parking lots and commercial buildings affect air quality and from what distance) representing mobile, stationary and area natural and anthropogenic air pollution sources. This approach is particularly important for enabling the construction of context-specific spatiotemporal models of air pollution, allowing investigations of the impact of air pollution exposures on sensitive populations such as children with asthma at scale.

Assessing the Potential of Land Use Modification to Mitigate Ambient NO₂ and Its Consequences for Respiratory Health

Understanding how local land use and land cover (LULC) shapes intra-urban concentrations of atmospheric pollutants-and thus human health-is a key component in designing healthier cities. Here, NO₂ is modeled based on spatially dense summer and winter NO₂ observations in Portland-Hillsboro-Vancouver (USA), and the spatial variation of NO₂ with LULC investigated using random forest, an ensemble data learning technique. The NO2 random forest model, together with BenMAP, is further used to develop a better understanding of the relationship among LULC, ambient NO₂ and respiratory health. The impact of land use modifications on ambient NO₂, and consequently on respiratory health, is also investigated using a sensitivity analysis. We find that NO₂ associated with roadways and tree-canopied areas may be affecting annual incidence rates of asthma exacerbation in 4-12 year olds by +3000 per 100,000 and -1400 per 100,000, respectively. Our model shows that increasing local tree canopy by 5% may reduce local incidences rates of asthma exacerbation by 6%, indicating that targeted local tree-planting efforts may have a substantial impact on reducing city-wide incidence of respiratory distress. Our findings demonstrate the utility of random forest modeling in evaluating LULC modifications for enhanced respiratory health.

Modeling the intraurban variation in nitrogen dioxide in urban areas in Kathmandu Valley, Nepal

BACKGROUND

With growing urbanization, traffic has become one of the main sources of air pollution in Nepal. Understanding the impact of air pollution on health requires estimation of exposure. Land use regression (LUR) modeling is widely used to investigate intraurban variation in air pollution for Western cities, but LUR models are relatively scarce in developing countries. In this study, we developed LUR models to characterize intraurban variation of nitrogen dioxide (NO₂) in urban areas of Kathmandu Valley, Nepal, one of the fastest urbanizing areas in South Asia.

METHODS

Over the study area, 135 monitoring sites were selected using stratified random sampling based on building density and road density along with purposeful sampling. In 2014, four sampling campaigns were performed, one per season, for two weeks each. NO₂ was measured using duplicate Palmes tubes at 135 sites, with additional information on nitric oxide (NO), NO₂, and nitrogen oxide (NOx) concentrations derived from Ogawa badges at 28 sites. Geographical variables (e.g., road network, land use, built area) were used as predictor variables in LUR modeling, considering buffers 25-400m around each monitoring site.

RESULTS

Annual average NO₂ by site ranged from 5.7 to 120ppb for the study area, with higher concentrations in the Village Development Committees (VDCs) of Kathmandu and Lalitpur than in Kirtipur, Thimi, and Bhaktapur, and with variability present within each VDC. In the final LUR model, length of major road, built area, and industrial area were positively associated with NO₂ concentration while normalized difference vegetation index (NDVI) was negatively associated with NO₂ concentration (R²=0.51). Cross-validation of the results confirmed the reliability of the model.

CONCLUSIONS

The combination of passive NO₂ sampling and LUR modeling techniques allowed for characterization of nitrogen dioxide patterns in a developing country setting, demonstrating spatial variability and high pollution levels.

Use of mobile and passive badge air monitoring data for NOX and ozone air pollution spatial exposure prediction models

Air pollution exposure prediction models can make use of many types of air monitoring data. Fixed location passive samples typically measure concentrations averaged over several days to weeks. Mobile monitoring data can generate near continuous concentration measurements. It is not known whether mobile monitoring data are suitable for generating well-performing exposure prediction models or how they compare with other types of monitoring data in generating exposure models. Measurements from fixed site passive samplers and mobile monitoring platform were made over a 2-week period in Baltimore in the summer and winter months in 2012. Performance of exposure prediction models for long-term nitrogen oxides (NO_X) and ozone (O₃) concentrations were compared using a state-of-the-art approach for model development based on land use regression (LUR) and geostatistical smoothing. Model performance was evaluated using leave-one-out cross-validation (LOOCV). Models performed well using the mobile peak traffic monitoring data for both NO_X and O₃, with LOOCV R²s of 0.70 and 0.71, respectively, in the summer, and 0.90 and 0.58, respectively, in the winter. Models using 2-week passive samples for NO_X had LOOCV R²s of 0.60 and 0.65 in the summer and winter months, respectively. The passive badge sampling data were not adequate for developing models for O₃. Mobile air monitoring data can be used to successfully build well-performing LUR exposure prediction models for NO_X and O₃ and are a better source of data for these models than 2-week passive badge data.

Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches

Exposure assessment for elemental components of particulate matter (PM) using land use modeling is a complex problem due to the high spatial and temporal variations in pollutant concentrations at the local scale. Land use regression (LUR) models may fail to capture complex interactions and non-linear relationships between pollutant concentrations and land use variables. The increasing availability of big spatial data and machine learning methods present an opportunity for improvement in PM exposure assessment models. In this manuscript, our objective was to develop a novel land use random forest (LURF) model and compare its accuracy and precision to a LUR model for elemental components of PM in the urban city of Cincinnati, Ohio. PM smaller than 2.5 μm (PM2.5) and eleven elemental components were measured at 24 sampling stations from the Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS). Over 50 different predictors associated with transportation, physical features, community socioeconomic characteristics, greenspace, land cover, and emission point sources were used to construct LUR and LURF models. Cross validation was used to quantify and compare model performance. LURF and LUR models were created for aluminum (Al), copper (Cu), iron (Fe), potassium (K), manganese (Mn), nickel (Ni), lead (Pb), sulfur (S), silicon (Si), vanadium (V), zinc (Zn), and total PM2.5 in the CCAAPS study area. LURF utilized a more diverse and greater number of predictors than LUR and LURF models for Al, K, Mn, Pb, Si, Zn, TRAP, and PM2.5 all showed a decrease in fractional predictive error of at least 5% compared to their LUR models. LURF models for Al, Cu, Fe, K, Mn, Pb, Si, Zn, TRAP, and PM2.5 all had a cross validated fractional predictive error less than 30%. Furthermore, LUR models showed a differential exposure assessment bias and had a higher prediction error variance. Random forest and other machine learning methods may provide more accurate exposure assessment.

Human Exposure Assessment for Air Pollution

Assessment of human exposure to air pollution is a fundamental part of the more general process of health risk assessment. The measurement methods for exposure assessment now include personal exposure monitoring, indoor-outdoor sampling, mobile monitoring, and exposure assessment modeling (such as proximity models, interpolation model, air dispersion models, and land-use regression (LUR) models). Among these methods, personal exposure measurement is considered to be the most accurate method of pollutant exposure assessment until now, since it can better quantify observed differences and better reflect exposure among smaller groups of people at ground level. And since the great differences of geographical environment, source distribution, pollution characteristics, economic conditions, and living habits, there is a wide range of differences between indoor, outdoor, and individual air pollution exposure in different regions of China. In general, the indoor particles in most Chinese families comprise infiltrated outdoor particles, particles generated indoors, and a few secondary organic aerosol particles, and in most cases, outdoor particle pollution concentrations are a major contributor to indoor concentrations in China. Furthermore, since the time, energy, and expense are limited, it is difficult to measure the concentration of pollutants for each individual. In recent years, obtaining the concentration of air pollutants by using a variety of exposure assessment models is becoming a main method which could solve the problem of the increasing number of individuals in epidemiology studies.

Spatial variation in diesel-related elemental and organic PM2.5 components during workweek hours across a downtown core

Capturing intra-urban variation in diesel-related pollution exposures remains a challenge, given its complex chemical mix, and relatively few well-characterized ambient-air tracers for the multiple diesel sources in densely-populated urban areas. To capture fine-scale spatial resolution (50×50m grid cells) in diesel-related pollution, we used geographic information systems (GIS) to systematically allocate 36 sampling sites across downtown Pittsburgh, PA, USA (2.8km²), cross-stratifying to disentangle source impacts (i.e., truck density, bus route frequency, total traffic density). For buses, outbound and inbound trips per week were summed by route and a kernel density was calculated across sites. Programmable monitors collected fine particulate matter (PM_2.5) samples specific to workweek hours (Monday-Friday, 7 am-7 pm), summer and winter 2013. Integrated filters were analyzed for black carbon (BC), elemental carbon (EC), organic carbon (OC), elemental constituents, and diesel-related organic compounds [i.e., polycyclic aromatic hydrocarbons (PAHs), hopanes, steranes]. To our knowledge, no studies have collected this suite of pollutants with such high sampling density, with the ability to capture spatial patterns during specific hours of interest. We hypothesized that we would find substantial spatial variation for each pollutant and significant associations with key sources (e.g. diesel and gasoline vehicles), with higher concentrations near the center of this small downtown core. Using a forward stepwise approach, we developed seasonal land use regression (LUR) models for PM_2.5, BC, total EC, OC, PAHs, hopanes, steranes, aluminum (Al), calcium (Ca), and iron (Fe). Within this small domain, greater concentration differences were observed in most pollutants across sites, on average, than between seasons. Higher PM_2.5 and BC concentrations were found in the downtown core compared to the boundaries. PAHs, hopanes, and steranes displayed different spatial patterning across the study area by constituent. Most LUR models suggested a strong influence of bus-related emissions on pollution gradients. Buses were more dominant predictors compared to truck and vehicular traffic for several pollutants. Overall, we found substantial variation in diesel-related concentrations in a very small downtown area, which varied across elemental and organic components.

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.

Developing Street-Level PM2.5 and PM10 Land Use Regression Models in High-Density Hong Kong with Urban Morphological Factors

Monitoring street-level particulates is essential to air quality management but challenging in high-density Hong Kong due to limitations in local monitoring network and the complexities of street environment. By employing vehicle-based mobile measurements, land use regression (LUR) models were developed to estimate the spatial variation of PM2.5 and PM10 in the downtown area of Hong Kong. Sampling runs were conducted along routes measuring a total of 30 km during a selected measurement period of total 14 days. In total, 321 independent variables were examined to develop LUR models by using stepwise regression with PM2.5 and PM10 as dependent variables. Approximately, 10% increases in the model adjusted R(2) were achieved by integrating urban/building morphology as independent variables into the LUR models. Resultant LUR models show that the most decisive factors on street-level air quality in Hong Kong are frontal area index, an urban/building morphological parameter, and road network line density and traffic volume, two parameters of road traffic. The adjusted R(2) of the final LUR models of PM2.5 and PM10 are 0.633 and 0.707, respectively. These results indicate that urban morphology is more decisive to the street-level air quality in high-density cities than other cities. Air pollution hotspots were also identified based on the LUR mapping.

Spatial variation in inversion-focused vs 24-h integrated samples of PM2.5 and black carbon across Pittsburgh, PA

A growing literature explores intra-urban variation in pollution concentrations. Few studies, however, have examined spatial variation during "peak" hours of the day (e.g., rush hours, inversion conditions), which may have strong bearing for source identification and epidemiological analyses. We aimed to capture "peak" spatial variation across a region of complex terrain, legacy industry, and frequent atmospheric inversions. We hypothesized stronger spatial contrast in concentrations during hours prone to atmospheric inversions and heavy traffic, and designed a 2-year monitoring campaign to capture spatial variation in fine particles (PM2.5) and black carbon (BC). Inversion-focused integrated monitoring (0600-1100 hours) was performed during year 1 (2011-2012) and compared with 1-week 24-h integrated results from year 2 (2012-2013). To allocate sampling sites, we explored spatial distributions in key sources (i.e., traffic, industry) and potential modifiers (i.e., elevation) in geographic information systems (GIS), and allocated 37 sites for spatial and source variability across the metropolitan domain (~388 km(2)). Land use regression (LUR) models were developed and compared by pollutant, season, and sampling method. As expected, we found stronger spatial contrasts in PM2.5 and BC using inversion-focused sampling, suggesting greater differences in peak exposures across urban areas than is captured by most integrated saturation campaigns. Temporal variability, commercial and industrial land use, PM2.5 emissions, and elevation were significant predictors, but did not more strongly predict concentrations during peak hours.

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.

Computation of geographic variables for air pollution prediction models in South Korea

Recent cohort studies have relied on exposure prediction models to estimate individuallevel air pollution concentrations because individual air pollution measurements are not available for cohort locations. For such prediction models, geographic variables related to pollution sources are important inputs. We demonstrated the computation process of geographic variables mostly recorded in 2010 at regulatory air pollution monitoring sites in South Korea. On the basis of previous studies, we finalized a list of 313 geographic variables related to air pollution sources in eight categories including traffic, demographic characteristics, land use, transportation facilities, physical geography, emissions, vegetation, and altitude. We then obtained data from different sources such as the Statistics Geographic Information Service and Korean Transport Database. After integrating all available data to a single database by matching coordinate systems and converting non-spatial data to spatial data, we computed geographic variables at 294 regulatory monitoring sites in South Korea. The data integration and variable computation were performed by using ArcGIS version 10.2 (ESRI Inc., Redlands, CA, USA). For traffic, we computed the distances to the nearest roads and the sums of road lengths within different sizes of circular buffers. In addition, we calculated the numbers of residents, households, housing buildings, companies, and employees within the buffers. The percentages of areas for different types of land use compared to total areas were calculated within the buffers. For transportation facilities and physical geography, we computed the distances to the closest public transportation depots and the boundary lines. The vegetation index and altitude were estimated at a given location by using satellite data. The summary statistics of geographic variables in Seoul across monitoring sites showed different patterns between urban background and urban roadside sites. This study provided practical knowledge on the computation process of geographic variables in South Korea, which will improve air pollution prediction models and contribute to subsequent health analyses.

Effect of Electrode Configuration on Nitric Oxide Gas Sensor Behavior

The influence of electrode configuration on the impedancemetric response of nitric oxide (NO) gas sensors was investigated for solid electrochemical cells [Au/yttria-stabilized zirconia (YSZ)/Au)]. Fabrication of the sensors was carried out at 1050 °C in order to establish a porous YSZ electrolyte that enabled gas diffusion. Two electrode configurations were studied where Au wire electrodes were either embedded within or wrapped around the YSZ electrolyte. The electrical response of the sensors was collected via impedance spectroscopy under various operating conditions where gas concentrations ranged from 0 to 100 ppm NO and 1%–18% O₂ at temperatures varying from 600 to 700 °C. Gas diffusion appeared to be a rate-limiting mechanism in sensors where the electrode configuration resulted in longer diffusion pathways. The temperature dependence of the NO sensors studied was independent of the electrode configuration. Analysis of the impedance data, along with equivalent circuit modeling indicated the electrode configuration of the sensor effected gas and ionic transport pathways, capacitance behavior, and NO sensitivity.

Establishing integrated rural-urban cohorts to assess air pollution-related health effects in pregnant women, children and adults in Southern India: an overview of objectives, design and methods in the Tamil Nadu Air Pollution and Health Effects (TAPHE) study

INTRODUCTION

In rapidly developing countries such as India, the ubiquity of air pollution sources in urban and rural communities often results in ambient and household exposures significantly in excess of health-based air quality guidelines. Few efforts, however, have been directed at establishing quantitative exposure-response relationships in such settings. We describe study protocols for The Tamil Nadu Air Pollution and Health Effects (TAPHE) study, which aims to examine the association between fine particulate matter (PM2.5) exposures and select maternal, child and adult health outcomes in integrated rural-urban cohorts.

METHODS AND ANALYSES

The TAPHE study is organised into five component studies with participants drawn from a pregnant mother-child cohort and an adult cohort (n=1200 participants in each cohort). Exposures are assessed through serial measurements of 24-48 h PM2.5 area concentrations in household microenvironments together with ambient measurements and time-activity recalls, allowing exposure reconstructions. Generalised additive models will be developed to examine the association between PM2.5 exposures, maternal (birth weight), child (acute respiratory infections) and adult (chronic respiratory symptoms and lung function) health outcomes while adjusting for multiple covariates. In addition, exposure models are being developed to predict PM2.5 exposures in relation to household and community level variables as well as to explore inter-relationships between household concentrations of PM2.5 and air toxics. Finally, a bio-repository of peripheral and cord blood samples is being created to explore the role of gene-environment interactions in follow-up studies.

ETHICS AND DISSEMINATION

The study protocols have been approved by the Institutional Ethics Committee of Sri Ramachandra University, the host institution for the investigators in this study. Study results will be widely disseminated through peer-reviewed publications and scientific presentations. In addition, policy-relevant recommendations are also being planned to inform ongoing national air quality action plans concerning ambient and household air pollution.

Residential proximity to major roads and obstetrical complications

Exposure to air pollution is linked with an increased risk of preterm births. To provide further evidence on this relationship, we evaluated the association between proximity to major roads--as an index for air pollution exposure--and various obstetrical complications. Data were extracted from a database maintained by the perinatal hospital in Shizuoka, Japan. We restricted the analysis to mothers with singleton pregnancies of more than 22 weeks of gestation from 1997 to 2012 (n=19,077). Using the geocoded residential information, each mother was assigned proximity to major roads. We then estimated multivariate adjusted odds ratios and their 95% confidence intervals (CIs) for the effects of proximity to major roads on various obstetrical complications (preeclampsia, gestational diabetes mellitus, placenta abruption, placenta previa, preterm premature rupture of membrane (pPROM), preterm labor, and preterm births). We found positive associations of proximity to major roads with preeclampsia and pPROM. Living within 200 m increased the odds of preeclampsia by 1.3 times (95% CI, 1.0-1.8) and pPROM by 1.6 times (95% CI, 1.1-2.2). Furthermore, living within 200 m increased the odds of preterm births by 1.4 fold (95% CI, 1.2-1.7). Exposure to traffic-related air pollution increased the risk of preeclampsia and pPROM in this study. We propose a mechanism responsible for the association between air pollution and preterm births.

Saturation sampling for spatial variation in multiple air pollutants across an inversion-prone metropolitan area of complex terrain

BACKGROUND

Characterizing intra-urban variation in air quality is important for epidemiological investigation of health outcomes and disparities. To date, however, few studies have been designed to capture spatial variation during select hours of the day, or to examine the roles of meteorology and complex terrain in shaping intra-urban exposure gradients.

METHODS

We designed a spatial saturation monitoring study to target local air pollution sources, and to understand the role of topography and temperature inversions on fine-scale pollution variation by systematically allocating sampling locations across gradients in key local emissions sources (vehicle traffic, industrial facilities) and topography (elevation) in the Pittsburgh area. Street-level integrated samples of fine particulate matter (PM2.5), black carbon (BC), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3) were collected during morning rush and probable inversion hours (6-11 AM), during summer and winter. We hypothesized that pollution concentrations would be: 1) higher under inversion conditions, 2) exacerbated in lower-elevation areas, and 3) vary by season.

RESULTS

During July - August 2011 and January - March 2012, we observed wide spatial and seasonal variability in pollution concentrations, exceeding the range measured at regulatory monitors. We identified elevated concentrations of multiple pollutants at lower-elevation sites, and a positive association between inversion frequency and NO2 concentration. We examined temporal adjustment methods for deriving seasonal concentration estimates, and found that the appropriate reference temporal trend differs between pollutants.

CONCLUSIONS

Our time-stratified spatial saturation approach found some evidence for modification of inversion-concentration relationships by topography, and provided useful insights for refining and interpreting GIS-based pollution source indicators for Land Use Regression modeling.

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

Although ground measurements have contributed to revealing the association between ambient air pollution and health effects in epidemiological studies, exposure measurement errors are likely to be caused because of the sparse spatial distribution of ground monitors. In this study, we estimate daily ground NO2 concentrations in the New England region, U.S., for the period 2005-2010 using satellite remote sensing data in combination with land use regression. To estimate ground-level NO2 concentrations, we constructed a mixed effects model by taking advantage of spatial and temporal variability in satellite Ozone Monitoring Instrument (OMI) tropospheric column NO2 densities. Using fine-scale land use parameters, we derived NO2 concentrations at point locations, which can be further used for subject-specific exposure estimates in epidemiological studies. A mixed effects model showed a reasonably high predictive power for daily NO2 concentrations (cross-validation R(2) = 0.79). We observed that the model performed similarly in each season, year, and state. The spatial patterns of model estimates reflected emission source areas (such as high populated/traffic areas) in the study region and revealed the seasonal characteristics of NO2. This study suggests that a combination of satellite remote sensing and land use regression can be useful for both spatially and temporally resolved exposure assessments of NO2.

ESTIMATING DAILY NITROGEN DIOXIDE LEVEL: EXPLORING TRAFFIC EFFECTS

Data used to assess acute health effects from air pollution typically have good temporal but poor spatial resolution or the opposite. A modified longitudinal model was developed that sought to improve resolution in both domains by bringing together data from three sources to estimate daily levels of nitrogen dioxide (NO₂) at a geographic location. Monthly NO₂ measurements at 316 sites were made available by the Study of Traffic, Air quality and Respiratory health (STAR). Four US Environmental Protection Agency monitoring stations have hourly measurements of NO₂. Finally, the Connecticut Department of Transportation provides data on traffic density on major roadways, a primary contributor to NO₂ pollution. Inclusion of a traffic variable improved performance of the model, and it provides a method for estimating exposure at points that do not have direct measurements of the outcome. This approach can be used to estimate daily variation in levels of NO₂ over a region.

Land use patterns and SO2 and NO2 pollution in Ulaanbaatar, Mongolia

We proposed to study spatial distribution and source contribution of SO2 and NO2 pollution in Ulaanbaatar, Mongolia. We collected 2-week ambient SO2 and NO2 concentration samples at 38 sites, which were classified by major sources of air pollution such as ger areas and/or major roads, in three seasons as warm (September, 2011), cold (November-December, 2011), and moderate (March, 2012) in Ulaanbaatar. The SO2 and NO2 concentrations were collected by Ogawa ambient air passive samplers and analyzed by ion chromatography and spectrophotometry methods, respectively. Stepwise regression models were used to estimate the contribution of emission proxies, such as the distance to major roads, ger areas, power plants, and city center, to the ambient concentrations of SO2 and NO2. We found that the SO2 and NO2 concentrations were significantly higher in the cold season than in the warm and moderate seasons at all 38 ambient sampling sites. The SO2 concentrations in 20 ger sites (46.60 ppb in the cold season and 17.82 ppb in the moderate season) were significantly higher than in 18 non-ger sites (23.35 ppb in the cold season and 12.53 ppb in the moderate season). The NO2 concentrations at 19 traffic/road sites (12.85 ppb in the warm season and 20.48 ppb in the moderate season) were significantly higher than those at 19 urban sites (7.60 ppb and 14.39 ppb in the moderate season). Multiple regression models show that SO2 concentrations decreased by 23% in the cold and 17% in the moderate seasons at 0.70 km from the ger areas, an average of all sampling sites, and by 29% in the moderate season at 4.83 km from the city center, an average of all sampling sites. Multiple regression models show that the NO2 concentrations at 4.83 km from the city center decreased by 38% in the warm and 29% in the moderate seasons. Our models also report that NO2 concentrations at 0.16 km from the main roads decreased by 15% and 9% in the warm and the moderate seasons, respectively, and by 16% in the cold season decreased at the location 0.70 km from the ger area. The NO2 concentration at the location 4.83 km from the city center was decreased by 18% and at the location 4.79 km from the power plants by 21%. Our study concludes that SO2 and NO2 concentrations are very high in Ulaanbaatar, especially in the winter, and can be explained by several land use variables, including the distance to the ger areas, the city center, the main roads, and the power plants.

Development of temporally refined land-use regression models predicting daily household-level air pollution in a panel study of lung function among asthmatic children

Regulatory monitoring data and land-use regression (LUR) models have been widely used for estimating individual exposure to ambient air pollution in epidemiologic studies. However, LUR models lack fine-scale temporal resolution for predicting acute exposure and regulatory monitoring provides daily concentrations, but fails to capture spatial variability within urban areas. This study coupled LUR models with continuous regulatory monitoring to predict daily ambient nitrogen dioxide (NO(2)) and particulate matter (PM(2.5)) at 50 homes in Windsor, Ontario. We compared predicted versus measured daily outdoor concentrations for 5 days in winter and 5 days in summer at each home. We also examined the implications of using modeled versus measured daily pollutant concentrations to predict daily lung function among asthmatic children living in those homes. Mixed effect analysis suggested that temporally refined LUR models explained a greater proportion of the spatial and temporal variance in daily household-level outdoor NO(2) measurements compared with daily concentrations based on regulatory monitoring. Temporally refined LUR models captured 40% (summer) and 10% (winter) more of the spatial variance compared with regulatory monitoring data. Ambient PM(2.5) showed little spatial variation; therefore, daily PM(2.5) models were similar to regulatory monitoring data in the proportion of variance explained. Furthermore, effect estimates for forced expiratory volume in 1 s (FEV(1)) and peak expiratory flow (PEF) based on modeled pollutant concentrations were consistent with effects based on household-level measurements for NO(2) and PM(2.5). These results suggest that LUR modeling can be combined with continuous regulatory monitoring data to predict daily household-level exposure to ambient air pollution. Temporally refined LUR models provided a modest improvement in estimating daily household-level NO(2) compared with regulatory monitoring data alone, suggesting that this approach could potentially improve exposure estimation for spatially heterogeneous pollutants. These findings have important implications for epidemiologic studies - in particular, for research focused on short-term exposure and health effects.

Monitoring intraurban spatial patterns of multiple combustion air pollutants in New York City: design and implementation

Routine air monitoring provides data to assess urban scale temporal variation in pollution concentrations in relation to regulatory standards, but is not well suited to characterizing intraurban spatial variation in pollutant concentrations from local sources. To address these limitations and inform local control strategies, New York City developed a program to track spatial patterns of multiple air pollutants in each season of the year. Monitor locations include 150 distributed street-level sites chosen to represent a range of traffic, land-use and other characteristics. Integrated samples are collected at each distributed site for one 2-week session each season and in every 2-week period at five reference locations to track city-wide temporal variation. Pollutants sampled include PM(2.5) and constituents, nitrogen oxides, black carbon, ozone (summer only) and sulfur dioxide (winter only). During the first full year of monitoring more than 95% of designed samples were completed. Agreement between colocated samples was good (absolute mean % difference 3.2-8.9%). Street-level pollutant concentrations spanned a much greater range than did concentrations at regulatory monitors, especially for oxides of nitrogen and sulfur dioxide. Monitoring to characterize intraurban spatial gradients in ambient pollution usefully complements regulatory monitoring data to inform local air quality management.