Annual Average NO2 Concentrations Research Articles

Impacts of urban form on air quality in metropolitan areas in the United States

Among 221 metropolitan areas (MAs) in the United States (US), this study explored the impact of urban form, either urban compactness or urban sprawl, on two types of air quality in 2014: NOx emissions from road traffic and annual average NO2 concentrations. Urban form was quantified using Smart Growth America (SGA) sprawl indexes with density, land use mixing, centeredness, and street connectivity. NOx emissions from road traffic were derived from the National Emissions Inventory (NEI). Through modeling NO2 concentrations using land use regression (LUR), with satellite-based estimates and kriging, this study measured NO2 concentrations within MAs in the US The study results showed that higher levels of urban form scores (i.e., higher compactness) and land use mixing were associated with lower per-person NOx emissions from road traffic. In addition, higher levels of centeredness were associated with lower NO2 concentrations, but the effect was moderate. On the other hand, regional rainfall and solar insolation had more significant associations with NO2 concentrations than metropolitan urban form. Meanwhile, localized emissions sources had significant associations with local-level NO2 concentrations. This study provides additional evidence on the relationship between urban form and air quality in the US MAs. The study suggests that high compactness-oriented development and the reduction of localized emission sources may be effective in reducing NOx emissions from road traffic and local NO2 concentrations, respectively. However, future studies need to explore the impact of urban form at both the MA and local levels on NO2 concentrations and develop a more accurate national NO2 concentration prediction model.

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

Paediatric asthma incidence is associated with exposure to traffic-related air pollution (TRAP), but the TRAP-attributable burden remains poorly quantified. Nitrogen dioxide (NO2) is a major component and common proxy of TRAP. In this study, we estimated the annual global number of new paediatric asthma cases attributable to NO2 exposure at a resolution sufficient to resolve intra-urban exposure gradients. We obtained 2015 country-specific and age-group-specific asthma incidence rates from the Institute for Health Metrics and Evaluation for 194 countries and 2015 population counts at a spatial resolution of 250 × 250 m from the Global Human Settlement population grid. We used 2010-12 annual average surface NO2 concentrations derived from land-use regression at a resolution of 100 × 100 m, and we derived concentration-response functions from relative risk estimates reported in a multinational meta-analysis. We then estimated the NO2-attributable burden of asthma incidence in children aged 1-18 years in 194 countries and 125 major cities at a resolution of 250 × 250 m. Globally, we estimated that 4·0 million (95% uncertainty interval [UI] 1·8-5·2) new paediatric asthma cases could be attributable to NO2 pollution annually; 64% of these occur in urban centres. This burden accounts for 13% (6-16) of global incidence. Regionally, the greatest burdens of new asthma cases associated with NO2 exposure per 100 000 children were estimated for Andean Latin America (340 cases per year, 95% UI 150-440), high-income North America (310, 140-400), and high-income Asia Pacific (300, 140-370). Within cities, the greatest burdens of new asthma cases associated with NO2 exposure per 100 000 children were estimated for Lima, Peru (690 cases per year, 95% UI 330-870); Shanghai, China (650, 340-770); and Bogota, Colombia (580, 270-730). Among 125 major cities, the percentage of new asthma cases attributable to NO2 pollution ranged from 5·6% (95% UI 2·4-7·4) in Orlu, Nigeria, to 48% (25-57) in Shanghai, China. This contribution exceeded 20% of new asthma cases in 92 cities. We estimated that about 92% of paediatric asthma incidence attributable to NO2 exposure occurred in areas with annual average NO2 concentrations lower than the WHO guideline of 21 parts per billion. Efforts to reduce NO2 exposure could help prevent a substantial portion of new paediatric asthma cases in both developed and developing countries, and especially in urban areas. Traffic emissions should be a target for exposure-mitigation strategies. The adequacy of the WHO guideline for ambient NO2 concentrations might need to be revisited. George Washington University.

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

BackgroundMethods for estimating air pollutant exposures for epidemiological studies are becoming more complex in an effort to minimise exposure error and its associated bias. While land use regression (LUR) modelling is now an established method, there has been little comparison between LUR and other recent, more complex estimation methods. Our aim was to develop a LUR model to estimate intra-city exposures to nitrogen dioxide (NO2) for a Sydney cohort, and to compare those with estimates from a national satellite-based LUR model (Sat-LUR) and a regional Bayesian Maximum Entropy (BME) model. MethodsSatellite-based LUR and BME estimates were obtained using existing models. We used methods consistent with the European Study of Cohorts for Air Pollution Effects (ESCAPE) methodology to develop LUR models for NO2 and NOx. We deployed 46 Ogawa passive samplers across western Sydney during 2013/2014 and acquired data on land use, population density, and traffic volumes for the study area. Annual average NO2 concentrations for 2013 were estimated for 947 addresses in the study area using the three models: standard LUR, Sat-LUR and a BME model. Agreement between the estimates from the three models was assessed using interclass correlation coefficient (ICC), Bland-Altman methods and correlation analysis (CC). ResultsThe NO2 LUR model predicted 84% of spatial variability in annual mean NO2 (RMSE: 1.2 ppb; cross-validated R2: 0.82) with predictors of major roads, population and dwelling density, heavy traffic and commercial land use. A separate model was developed that captured 92% of variability in NOx (RMSE 2.3 ppb; cross-validated R2: 0.90). The annual average NO2 concentrations were 7.31 ppb (SD: 1.91), 7.01 ppb (SD: 1.92) and 7.90 ppb (SD: 1.85), for the LUR, Sat-LUR and BME models respectively. Comparing the standard LUR with Sat-LUR NO2 cohort estimates, the mean estimates from the LUR were 4% higher than the Sat-LUR estimates, and the ICC was 0.73. The Pearson's correlation coefficients (CC) for the LUR vs Sat-LUR values were r = 0.73 (log-transformed data) and r = 0.69 (untransformed data). Comparison of the NO2 cohort estimates from the LUR model with the BME blended model indicated that the LUR mean estimates were 8% lower than the BME estimates. The ICC for the LUR vs BME estimates was 0.73. The CC for the logged LUR vs BME estimates was r = 0.73 and for the unlogged estimates was r = 0.69. ConclusionsOur LUR models explained a high degree of spatial variability in annual mean NO2 and NOx in western Sydney. The results indicate very good agreement between the intra-city LUR, national-scale sat-LUR, and regional BME models for estimating NO2 for a cohort of children residing in Sydney, despite the different data inputs and differences in spatial scales of the models, providing confidence in their use in epidemiological studies.

Estimating Ground-Level Concentrations of Multiple Air Pollutants and Their Health Impacts in the Huaihe River Basin in China.

Air pollutants existing in the environment may have negative impacts on human health depending on their toxicity and concentrations. Remote sensing data enable researchers to map concentrations of various air pollutants over vast areas. By combining ground-level concentrations with population data, the spatial distribution of health impacts attributed to air pollutants can be acquired. This study took five highly populated and severely polluted provinces along the Huaihe River, China, as the research area. The ground-level concentrations of four major air pollutants including nitrogen dioxide (NO2), sulfate dioxide (SO2), particulate matters with diameter equal or less than 10 (PM10) or 2.5 micron (PM2.5) were estimated based on relevant remote sensing data using the geographically weighted regression (GWR) model. The health impacts of these pollutants were then assessed with the aid of co-located gridded population data. The results show that the annual average concentrations of ground-level NO2, SO2, PM10, and PM2.5 in 2016 were 31 µg/m3, 26 µg/m3, 100 µg/m3, and 59 µg/m3, respectively. In terms of the health impacts attributable to NO2, SO2, PM10, and PM2.5, there were 546, 1788, 10,595, and 8364 respiratory deaths, and 1221, 9666, 46,954, and 39,524 cardiovascular deaths, respectively. Northern Henan, west-central Shandong, southern Jiangsu, and Wuhan City in Hubei are prone to large health risks. Meanwhile, air pollutants have an overall greater impact on cardiovascular disease than respiratory disease, which is primarily attributable to the inhalable particle matters. Our findings provide a good reference to local decision makers for the implementation of further emission control strategies and possible health impacts assessment.

Premature Adult Death and Equity Impact of a Reduction of NO₂, PM10, and PM2.5 Levels in Paris-A Health Impact Assessment Study Conducted at the Census Block Level.

Background: To support environmental policies aiming to tackle air pollution, quantitative health impact assessments (HIAs) stand out as one of the best decision-making tools. However, no risk assessment studies have quantified or mapped the health and equity impact of air pollution reduction at a small spatial scale. Objectives: We developed a small-area analysis of the impact of air pollution on “premature” death among an adult population over 30 years of age to quantify and map the health and equity impact related to a reduction of air pollution. Methods: All-cause mortality data of an adult population (>30 years) from January 2004 to December 2009 were geocoded at the residential census block level in Paris. Each census block was assigned socioeconomic deprivation levels and annual average ambient concentrations of NO2, PM10, and PM2.5. HIAs were used to estimate, at a small-area level, the number of “premature” deaths associated with a hypothetical reduction of NO2, PM10, and PM2.5 exposure. In total, considering global dose response function for the three pollutants and socioeconomic deprivation specific dose response function, nine HIAs were performed for NO2 and six and four HIAs for PM10 and PM2.5, respectively. Finally, a clustering approach was used to quantify how the number of “premature” deaths could vary according to deprivation level. Results: The number of deaths attributable to NO2, PM10, and PM2.5 exposure were equal to 4301, 3209, and 2662 deaths, respectively. The most deprived census blocks always appeared as one of the groups most impacted by air pollution. Our findings showed that “premature” deaths attributable to NO2 were not randomly distributed over the study area, with a cluster of excess “premature” deaths located in the northeastern area of Paris. Discussion: This study showed the importance of stratifying an environmental burden of disease study on the socioeconomic level, in order to take into consideration the modifier effect of socioeconomic status on the air pollution-mortality relationship. In addition, we demonstrated the value of spatial analysis to guide decision-making. This shows the need for tools to support priority-setting and to guide policymakers in their choice of environmental initiatives that would maximize health gains and reduce social inequalities in health.

Evaluate the Validity of Land Use Regression Models for NO2 Based on Routine and Campaign Monitoring Data from an Urban Area of Japan

Background: Generally, land use regression (LUR) models have been built using campaign monitoring data rather than routine monitoring data. However, the routine monitoring data have advantages such as low cost and long-term coverage. The objective of this study was to compare the predictability of LUR models for NO2 based on routine and campaign monitoring data obtained from an urban area. This evaluation considered that LUR models that could represent regional differences in air pollution exposure and regional road structure were optimal.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), and a model based on campaign monitoring data (campaign-LUR) within the city. The routine LUR models were based on monitoring networks across two prefectures (i.e., Osaka and Hyogo prefectures). Next, we selected 30 evaluation sites and developed LUR models excluding these evaluations sites. We calculated the predictability of each model, and compared the predicted NO2 concentrations from each model with measured annual average NO2 concentrations from evaluation sites.Results: The predictability for NO2 concentrations of routine-LUR model was better than that of campaign-LUR model: adjusted R2 = 0.68 and 0.59, respectively, and root mean square error = 3.4 ppb and 3.9 ppb, respectively. The routine-LUR model were highly correlated with the measured NO2 concentrations at evaluation sites [ρ = 0.88] comparing with the campaign-LUR model [ρ = 0.54]. Although the predicted NO2 concentrations from each model were correlated, the LUR models based on routine networks 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.

Using a Clustering Approach to Investigate Socio-Environmental Inequality in Preterm Birth-A Study Conducted at Fine Spatial Scale in Paris (France).

Background & Objectives: Today, to support public policies aiming to tackle environmental and health inequality, identification and monitoring of the spatial pattern of adverse birth outcomes are crucial. Spatial identification of the more vulnerable population to air pollution may orient health interventions. In this context, the objective of this study is to investigate the geographical distribution of the risk of preterm birth (PTB, gestational age ≤36 weeks) at the census block level in in city of Paris, France. We also aimed to assess the implication of neighborhood characteristics including air pollution and socio-economic deprivation. Material & Methods: Newborn health data are available from the first birth certificate registered by the Maternal and Child Care department of Paris. All PTB from January 2008 to December 2011 were geocoded at the mother residential census block. Each census block was assigned a socioeconomic deprivation level and annual average ambient concentrations of NO2. A spatial clustering approach was used to investigate the spatial distribution of PTB. Results: Our results highlight that PTB is non-randomly spatially distributed, with a cluster of high risk in the northeastern area of Paris (RR = 1.15; p = 0.06). After adjustment for socio-economic deprivation and NO2 concentrations, this cluster becomes not statistically significant or shifts suggesting that these characteristics explain the spatial distribution of PTB; further, their combination shows an interaction in comparison with SES or NO2 levels alone. Conclusions: Our results may inform the decision makers about the areas where public health efforts should be strengthened to tackle the risk of PTB and to choose the most appropriate and specific community-oriented health interventions.

Development of European NO2 Land Use Regression Model for present and future exposure assessment: Implications for policy analysis

A new Land Use Regression model was built to develop pan-European 100 m resolution maps of NO2 concentrations. The model was built using NO2 concentrations from routine monitoring stations available in the Airbase database as dependent variable. Predictor variables included land use, road traffic proxies, population density, climatic and topographical variables, and distance to sea. In order to capture international and inter regional disparities not accounted for with the mentioned predictor variables, additional proxies of NO2 concentrations, like levels of activity intensity and NOx emissions for specific sectors, were also included. The model was built using Random Forest techniques. Model performance was relatively good given the EU-wide scale (R2 = 0.53). Output predictions of annual average concentrations of NO2 were in line with other existing models in terms of spatial distribution and values of concentration. The model was validated for year 2015, comparing model predictions derived from updated values of independent variables, with concentrations in monitoring stations for that year. The algorithm was then used to model future concentrations up to the year 2030, considering different emission scenarios as well as changes in land use, population distribution and economic factors assuming the most likely socio-economic trends. Levels of exposure were derived from maps of concentration. The model proved to be a useful tool for the ex-ante evaluation of specific air pollution mitigation measures, and more broadly, for impact assessment of EU policies on territorial development.

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

BackgroundTypically, 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. MethodWe 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. ResultsThe 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. ConclusionThe 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.

Agreement between Local LUR and National Satellite-Based LUR Models for Estimating NO2 in Sydney, Australia

Background/Aim: Methods for estimating air pollutant exposures for epidemiological studies are rapidly evolving with more complex methods emerging. Our aim was to determine the agreement between two methods for estimating NO2 exposures for a children’s cohort (CAPS), based in Sydney, Australia. The two methods were a traditional city-scale land use regression (LUR) model and a national satellite-based LUR (Sat-LUR) model. Methods: We used methods consistent with ESCAPE methodology to develop a NO2 LUR model, using Ogawa passive sampler data collected at 46 locations across western Sydney (two week periods over three seasons in 2013 and 2014). We collected data on land use, population density, and traffic volumes as potential predictor variables. These LUR estimates were compared to those from a national Sat-LUR model, which used data from the OMI sensor on the Aura satellite and land use data to predict NO2 concentrations at 68 fixed-site monitors located across Australia. Each model was used to estimate annual average NO2 concentrations for 2013 at each of 964 cohort addresses. A Bland-Altman assessment was used to compare the estimates. Results: The traditional LUR model predicted 84% of variability in NO2 (adjusted R2=0.84; RMSE 1.2ppb; 82% cross validation) with predictors being major roads, population and dwelling density, heavy traffic and commercial land use. The Sat-LUR adjusted R2 was 0.81 (RMSE 1.4 ppb) and predictor variables included major roads, industrial emissions, industrial land use and open space. The annual average means were 7.3 (SD 1.95) and 6.9 (SD 1.96) respectively. Comparing the two sets of estimates, the mean difference was 0.31 ppb (CI0.209 to0.404), limits of agreement ranged from -2.78 to 3.40, and the ICC was 0.69. Conclusions: The results indicate very good agreement between traditional LUR and satellite-based methods for estimating NO2 in Sydney, providing confidence in their use for exposure-response analyses.

Bayesian Blending of Nitrogen Dioxide Air Pollution Data for Neighbourhood Estimates for Health Research

Background/Aim: Exposure to nitrogen dioxide (NO2) pollution has been associated with a range of adverse health outcomes. Annual average pollutant concentrations are often used to estimate exposure, however, these estimates can be imprecise due to difficulty modelling spatial patterns at the resolution of neighbourhoods (e.g. a scale of tens of metres) rather than at a coarse scale (around several kilometres resolution). The objective of this study was to derive improved estimates of neighbourhood level pollutant concentrations for health studies by blending air pollutant measurements with modelled predictions using Bayesian analyses. Methods: We implemented the Bayesian Maximum Entropy (BME) approach to blend 'hard' data (considered precise) and probabilistic 'soft' data (with uncertainty defined using informative priors). We compiled and harmonised NO2 data from fixed-site monitors, chemical transport models, and satellite-based land use regression models to estimate neighbourhood level annual average NO2 concentrations in Sydney, Australia. The spatial model integrated the different underlying probabilities to produce a posterior probability density function of neighbourhood exposures. The mean of the posterior density was our estimate of NO2 exposure. Results: Estimated annual average concentrations from the BME model ranged from 3 to 35 ppb.Validation using independent data from a separate set of samples (using passive sampling methods) showed improvement, with Root Mean Squared Error (RMSE) of 2.6 ppb compared with the land use regression (2.8 ppb) and chemical transport model (3.1 ppb). Conclusions: Our study implemented state-of-the-art methods for exposure assessment and demonstrated an improvement in validation test statistics. In future work we will explore the impact of these improvements on exposure misclassification bias when used in epidemiological analyses of the impact of air pollution on health.

Air pollution as a risk factor for increasing hospitalizations of preschool wheeze in Hong Kong

Background: Wheeze has been reported to affect one-third of preschoolchildren. While different wheeze patterns have been shown to predict future asthma risk, limited data are available on the risk factors for preschool wheeze in Asia. Methods: Preschool children admitted to hospitals through emergency departments for wheeze, from 2004 to 2015 in Hong Kong, were retrospectively identified. Potential risk factors for admissions over the same period were retrieved (i.e., air pollutants, preterm delivery, and maternal age). Results: A total of 46,258 patients meeting the inclusion criteria were identified during the 12-year period. The preschool wheeze admission rate increased by 34% over the past 12 years, with an average year-on-year rise of 4.2%. Environmental nitrogen dioxide (NO2) concentration was significantly associated with an increase in admission for preschool wheeze (r = 0.63, P = 0.028). Univariate regression analysis was performed on potential risk factors. Annual average NO2concentration (P = 0.007) and maternal age more than 40 years (P = 0.012) were significant risk factors. For multivariable regression analysis, annual average NO2concentration (β = 0.18, 95% confidence interval = 0.06–0.30) was the only independent factor associated with preschool wheeze admission. Conclusions: The increase of NO2concentration is a significant risk factor for the increase in hospitalizations for preschool wheeze in Hong Kong.

Changes in Transportation-Related Air Pollution Exposures by Race-Ethnicity and Socioeconomic Status: Outdoor Nitrogen Dioxide in the United States in 2000 and 2010.

Background:Disparities in exposure to air pollution by race-ethnicity and by socioeconomic status have been documented in the United States, but the impacts of declining transportation-related air pollutant emissions on disparities in exposure have not been studied in detail.Objective:This study was designed to estimate changes over time (2000 to 2010) in disparities in exposure to outdoor concentrations of a transportation-related air pollutant, nitrogen dioxide (), in the United States.Methods:We combined annual average concentration estimates from a temporal land use regression model with Census demographic data to estimate outdoor exposures by race-ethnicity, socioeconomic characteristics (income, age, education), and by location (region, state, county, urban area) for the contiguous United States in 2000 and 2010.Results:Estimated annual average concentrations decreased from 2000 to 2010 for all of the race-ethnicity and socioeconomic status groups, including a decrease from to () in nonwhite [non-(white alone, non-Hispanic)] populations, and to () in white (white alone, non-Hispanic) populations. In 2000 and 2010, disparities in concentrations were larger by race-ethnicity than by income. Although the national nonwhite–white mean concentration disparity decreased from a difference of in 2000 to in 2010, estimated mean concentrations remained 37% higher for nonwhites than whites in 2010 (40% higher in 2000), and nonwhites were 2.5 times more likely than whites to live in a block group with an average concentration above the WHO annual guideline in 2010 (3.0 times more likely in 2000).Conclusions:Findings suggest that absolute exposure disparities by race-ethnicity decreased from 2000 to 2010, but relative exposure disparities persisted, with higher concentrations for nonwhites than whites in 2010. https://doi.org/10.1289/EHP959

National-scale exposure prediction for long-term concentrations of particulate matter and nitrogen dioxide in South Korea

The limited spatial coverage of the air pollution data available from regulatory air quality monitoring networks hampers national-scale epidemiological studies of air pollution. The present study aimed to develop a national-scale exposure prediction model for estimating annual average concentrations of PM10 and NO2 at residences in South Korea using regulatory monitoring data for 2010. Using hourly measurements of PM10 and NO2 at 277 regulatory monitoring sites, we calculated the annual average concentrations at each site. We also computed 322 geographic variables in order to represent plausible local and regional pollution sources. Using these data, we developed universal kriging models, including three summary predictors estimated by partial least squares (PLS). The model performance was evaluated with fivefold cross-validation. In sensitivity analyses, we compared our approach with two alternative approaches, which added regional interactions and replaced the PLS predictors with up to ten selected variables. Finally, we predicted the annual average concentrations of PM10 and NO2 at 83,463 centroids of residential census output areas in South Korea to investigate the population exposure to these pollutants and to compare the exposure levels between monitored and unmonitored areas. The means of the annual average concentrations of PM10 and NO2 for 2010, across regulatory monitoring sites in South Korea, were 51.63 μg/m3 (SD = 8.58) and 25.64 ppb (11.05), respectively. The universal kriging exposure prediction models yielded cross-validated R2s of 0.45 and 0.82 for PM10 and NO2, respectively. Compared to our model, the two alternative approaches gave consistent or worse performances. Population exposure levels in unmonitored areas were lower than in monitored areas. This is the first study that focused on developing a national-scale point wise exposure prediction approach in South Korea, which will allow national exposure assessments and epidemiological research to answer policy-related questions and to draw comparisons among different countries.

Effect of air pollution on pregnancy outcome of women at reproductive age in Xi'an, 2010-2013

Objective: To compare the differences in the incidence of adverse pregnancy outcome in different area, and confirm if the incidence of adverse pregnancy outcomes is closely associated with air pollution. Methods: A cross-sectional study was conducted in the central urban area and the rural-urban area of Xi'an through a questionnaire survey conducted among the local reproductive women selected through multistage stratified random sampling during 2010-2013, all the reproductive women surveyed were in pregnancy or had definite pregnancy outcomes. Results: The annual average of SO2 concentration in the central urban area was 38-54 μg/m3, higher than that in the rural-urban area (29-43 μg/m3). The annual average NO2 concentration in the central urban area was 29-87 μg/m3, higher than that in the rural-urban area (22-42 μg/m3). The incidence of birth defects was higher in the central urban area than in the rural-urban area (2.1% vs. 1.0%), the difference was significant (P<0.05). After adjusting for confounding factors, logistic regression analysis showed the incidence of birth defects in the central urban area was still significantly higher than that in the rural-urban area, the difference was significant (P<0.05). Conclusion: The incidence of adverse pregnancy was closely associated with the level of air pollution.

Spatial variation in nitrogen dioxide concentrations and cardiopulmonary hospital admissions

BackgroundAir pollution episodes are associated with increased cardiopulmonary hospital admissions. Cohort studies showed associations of spatial variation in traffic-related air pollution with respiratory and cardiovascular mortality. Much less is known in particular about associations with cardiovascular morbidity. We explored the relation between spatial variation in nitrogen dioxide (NO2) concentrations and cardiopulmonary hospital admissions. MethodsThis ecological study was based on hospital admissions data (2001–2004) from the National Medical Registration and general population data for the West of the Netherlands (population 4.04 million). At the 4-digit postcode area level (n=683) associations between modeled annual average outdoor NO2 concentrations and hospital admissions for respiratory and cardiovascular causes were evaluated by linear regression with the log of the postcode-specific percentage of subjects that have been admitted at least once during the study period as the dependent variable. All analyses were adjusted for differences in composition of the population of the postcode areas (age, sex, income). ResultsAt the postcode level, positive associations were found between outdoor NO2 concentrations and hospital admission rates for asthma, chronic obstructive pulmonary disease (COPD), all cardiovascular causes, ischemic heart disease and stroke (e.g. adjusted relative risk (95% confidence interval) for the second to fourth quartile relative to the first quartile of exposure were 1.87 (1.46–2.40), 2.34 (1.83–3.01) and 2.81 (2.16–3.65) for asthma; 1.44 (1.19–1.74), 1.50 (1.24–1.82) and 1.60 (1.31–1.96) for COPD). Associations remained after additional (indirect) adjustment for smoking (COPD admission rate) and degree of urbanization. ConclusionsOur study suggests an increased risk of hospitalization for respiratory and cardiovascular causes in areas with higher levels of NO2. Our findings add to the currently limited evidence of a long-term effect of air pollution on hospitalization. The ecological design of our study is a limitation and more studies with individual data are needed to confirm our findings.

Characteristics of ammonia, acid gases, and PM2.5 for three typical land-use types in the North China Plain.

Air pollution is one of the most serious environmental problems in China due to its rapid economic development alongside a very large consumption of fossil fuel, particularly in the North China Plain (NCP). During the period 2011–2014, we integrated active and passive sampling methods to perform continuous measurements of NH3, HNO3, NO2, and PM2.5 at two urban, one suburban, and two rural sites in the NCP. The annual average concentrations of NH3, NO2, and HNO3 across the five sites were in the ranges 8.5–23.0, 22.2–50.5, and 5.5–9.7 μg m−3, respectively, showing no significant spatial differences for NH3 and HNO3 but significantly higher NO2 concentration at the urban sites. At each site, annual average concentrations of NH3 and NO2 showed increasing and decreasing trends, respectively, while there was no obvious trend in annual HNO3 concentrations. Daily PM2.5 concentrations ranged from 11.8 to 621.0 μg m−3 at the urban site, from 19.8 to 692.9 μg m−3 at the suburban site, and from 23.9 to 754.5 μg m−3 at the two rural sites, with more than 70 % of sampling days exceeding 75 μg m−3. Concentrations of water-soluble ions in PM2.5 ranked differently between the non-rural and rural sites. The three dominant ions were NH4+, NO3−, and SO42− and mainly existed as (NH4)2SO4, NH4HSO4, and NH4NO3, and their concentrations averaged 48.6 ± 44.9, 41.2 ± 40.8, and 49.6 ± 35.9 μg m−3 at the urban, suburban, and rural sites, respectively. Ion balance calculations indicated that PM2.5 was neutral at the non-rural sites but acidic at the rural sites. Seasonal variations of the gases and aerosols exhibited different patterns, depending on source emission strength and meteorological conditions. Our results suggest that a feasible pathway to control PM2.5 pollution in the NCP should target ammonia and acid gases together.Electronic supplementary materialThe online version of this article (doi:10.1007/s11356-015-5648-3) contains supplementary material, which is available to authorized users.

Long-term ambient air pollution exposure and self-reported morbidity in the Australian Longitudinal Study on Women's Health: a cross-sectional study.

ObjectiveWe sought to assess the effect of long-term exposure to ambient air pollution on the prevalence of self-reported health outcomes in Australian women.DesignCross-sectional study.Setting and participantsThe geocoded residential addresses of 26 991 women across 3 age cohorts in the Australian Longitudinal Study on Women's Health between 2006 and 2011 were linked to nitrogen dioxide (NO2) exposure estimates from a land-use regression model. Annual average NO2 concentrations and residential proximity to roads were used as proxies of exposure to ambient air pollution.Outcome measuresSelf-reported disease presence for diabetes mellitus, heart disease, hypertension, stroke, asthma, chronic obstructive pulmonary disease and self-reported symptoms of allergies, breathing difficulties, chest pain and palpitations.MethodsDisease prevalence was modelled by population-averaged Poisson regression models estimated by generalised estimating equations. Associations between symptoms and ambient air pollution were modelled by multilevel mixed logistic regression. Spatial clustering was accounted for at the postcode level.ResultsNo associations were observed between any of the outcome and exposure variables considered at the 1% significance level after adjusting for known risk factors and confounders.ConclusionsLong-term exposure to ambient air pollution was not associated with self-reported disease prevalence in Australian women. The observed results may have been due to exposure and outcome misclassification, lack of power to detect weak associations or an actual absence of associations with self-reported outcomes at the relatively low annual average air pollution exposure levels across Australia.

Development of Land Use Regression models for predicting exposure to NO2 and NOx in Metropolitan Perth, Western Australia

This study developed LUR models for predicting exposure to NO2 and NOx among of 12,203 elderly men in Perth. NOx and NO2 concentrations were determined for 2-week periods in summer, autumn, and winter, from January to September 2012, at 43 sites. The LUR models were developed to predict annual average concentrations of nitric oxides based upon land use, population/household density, and traffic variables within different buffer sizes, following the procedures of the European Study of Cohort for Air Pollution Effects program. The sample mean and standard deviation of the annual average concentrations of NO2 and NOx were 10.1 ± 5.3 μg/m3 and 18.7 ± 11.7 μg/m3 respectively, lower than those of ESCAPE study areas. The LUR models explained 69% of the variance in NO2 and 75% variance of NOx. Both the NO2 and NOx models had similar predictors, including traffic intensity on the nearest roads, household density within-1000 m industrial activities within-5000 m, and road length within-50 m.

Ground-level ozone in urban Beijing over a 1-year period: Temporal variations and relationship to atmospheric oxidation

Regional ozone pollution has become a major environmental concern in China, especially in densely populated and economically vibrant regions such as North China, including Beijing. To address this issue, surface ozone and its precursors (CO, NO, and NO2) from December 2012 to November 2013 at 12 sites in urban Beijing and 2 sites in suburban Beijing were analyzed. The annual average concentrations of O3, CO, NO, and NO2 in urban Beijing were 45.5±50.2μgm−3, 1.5±1.3mgm−3, 27.3±42.7μgm−3, and 58.3+32.0μgm−3, respectively. The concentration of ozone was highest during summer, whereas concentrations of its precursors were highest during winter. Diurnal variations in ozone presented as a single-peak curve, with the peak appearing at about 15:00–16:00. Diurnal variations in most ozone precursors showed bimodal curves; the first peak appeared at about 08:00–09:00, and the second peak appeared at night. Hourly concentrations of ozone on the weekend were higher than those on weekdays between 11:00 and 24:00 in urban Beijing, which was suggestive of a significant weekend effect. This may be because NO inhibition on the weekend is weaker than that on weekdays during the ozone formation phase. Diurnal variations in OX (NO2+O3) showed a single peak, which appeared at 15:00 or 16:00. The results of correlation analysis among OX, O3, and NO2 suggested that OX was mainly controlled by O3 during the day and by NO2 during the night throughout the year. OX was controlled by NO2 during both the day and night during winter due to the low concentration of O3. The regional transport of ozone along the upwind direction was found in a typical ozone pollution event in summer in Beijing.