Modeling horizontal and vertical variation in intraurban exposure to PM2.5 concentrations and compositions

A Review of Urban Planning Approaches to Reduce Air Pollution Exposures

PURPOSE OF REVIEW

With only 12% of the human population living in cities meeting the air quality standards set by the WHO guidelines, there is a critical need for coordinated strategies to meet the requirements of a healthy society. One pivotal mechanism for addressing societal expectations on air pollution and human health is to employ strategic modeling within the urban planning process. This review synthesizes research to inform coordinated strategies for a healthy society. Through strategic modeling in urban planning, we seek to uncover integrated solutions that mitigate air pollution, enhance public health, and create sustainable urban environments.

RECENT FINDINGS

Successful urban planning can help reduce air pollution by optimizing city design with regard to transportation systems. As one specific example, ventilation corridors i.e. aim to introduce natural wind into urban areas to improve thermal comfort and air quality, and they can be effective if well-designed and managed. However, physical barriers such as sound walls and vegetation must be carefully selected following design criteria with significant trade-offs that must be modeled quantitatively. These tradeoffs often involve balancing effectiveness, cost, aesthetics, and environmental impact. For instance, sound walls are highly effective at reducing noise, provide immediate impact, and are long-lasting. However, they are expensive to construct, visually unappealing, and may block views and sunlight. To address the costly issue of sound walls, a potential solution is implementing vegetation with a high leaf area index or leaf area density. This alternative is also an effective method for air pollution reduction with varying land-use potential. Ultimately, emission regulations are a key aspect of all such considerations. Given the broad range of developments, concerns, and considerations spanning city management, ventilation corridors, physical barriers, and transportation planning, this review aims to summarize the effect of a range of urban planning methods on air pollution considerations.

Effect of air quality improvement by urban parks on mitigating PM2.5 and its associated heavy metals: A mobile-monitoring field study

Field mobile monitoring of PM_2.5, equipped with a highly accurate device, was performed for two types of urban parks in Taiwan. Measurements were taken in the morning and evening rush hours, on certain weekdays and weekends, every month over a year. We designed six calculation schemes of the rate of PM_2.5 mitigation by urban parks to comprehensively compare the average and maximum mitigation effects in relation to the vegetation barriers. The mitigation rate, from the lowest (2.51%) to the highest (35.57%) depended on the calculation schemes. The Taipei Botanical Garden (TBG) with a dense, multilevel forest has a stable PM_2.5 mitigation effect and strong ability to improve air quality inside the park under severe PM_2.5 pollution. In contrast, Zhonghe No.4 Park (ZHP), an open park with mostly a single-storied stand, has variable PM_2.5 mitigation effect, leading to either quick dissipation or accumulation of PM_2.5 inside the park. Furthermore, the dry deposition of PM and the associated heavy metals were investigated using camphor trees as bioaccumulators. Dry deposition flux of the leaf-deposited PM_2.5 exhibited similar results in ZHP; whereas, noticeable higher results were observed inside TBG. In addition, most of the PM_2.5 deposition flux from field estimations were similar to those in i-Tree Eco when considering the loss of mass due to the dissolution through water filtration, indicating that i-Tree Eco may be reliable to model the removal of PM_2.5 in the parks in Taiwan. Moreover, we examined nine heavy metals' content in the deposited PM, and most of the detectable elements were significantly higher outside both parks, demonstrating the mitigation effects of urban parks in reducing not only the PM_2.5 concentration but also the toxicity of the pollutant. This study provides direct evidence of the important ecosystem services, namely air quality improvement and biomonitoring effect, derived from urban parks.

A Review of Road Traffic-Derived Non-Exhaust Particles: Emissions, Physicochemical Characteristics, Health Risks, and Mitigation Measures

Implementation of regulatory standards has reduced exhaust emissions of particulate matter from road traffic substantially in the developed world. However, nonexhaust particle emissions arising from the wear of brakes, tires, and the road surface, together with the resuspension of road dust, are unregulated and exceed exhaust emissions in many jurisdictions. While knowledge of the sources of nonexhaust particles is fairly good, source-specific measurements of airborne concentrations are few, and studies of the toxicology and epidemiology do not give a clear picture of the health risk posed. This paper reviews the current state of knowledge, with a strong focus on health-related research, highlighting areas where further research is an essential prerequisite for developing focused policy responses to nonexhaust particles.

Evaluation of Using Satellite-Derived Aerosol Optical Depth in Land Use Regression Models for Fine Particulate Matter and Its Elemental Composition

This study introduced satellite-derived aerosol optical depth (AOD) in land use regression (LUR) modeling to predict ambient concentrations of fine particulate matter (PM2.5) and its elemental composition. Twenty-four daily samples were collected from 17 air quality monitoring sites (N = 408) in Taiwan in 2014. A total of 12 annual LUR models were developed for PM2.5 and 11 elements, including aluminum, calcium, chromium, iron, potassium, manganese, sulfur, silicon, titanium, vanadium, and zinc. After applied AOD and a derived-predictor, AOD percentage, in modeling, the number of models with leave-one-out cross-validation R2 > 0.40 significantly increased from 5 to 9, indicating the substantial benefits for the construction of spatial prediction models. Sensitivity analyses of using data stratified by PM2.5 concentrations revealed that the model performances were further improved in the high pollution season.

Sources of 24-h personal exposure to PM2.5-bound metals: results from a panel study in Wuhan, China

Atmospheric PM2.5-bound metals have been widely addressed, but research on the exposure levels and sources of personal PM2.5-bound metals among urban community residents is limited. The aim of this study is to explore the exposure levels and sources of 24-h personal PM2.5-bound metals among community inhabitants in Wuhan, China. We conducted a penal study of 216 observations with measurements of 16 metals bounded to 24-h personal PM2.5 samples in April-May, 2014, 2017. Analyses of covariance were used to compare PM2.5-bound metal levels across different living habits and ambient conditions. Principal component analysis (PCA) with varimax rotation was performed to explore PM2.5-bound metal sources. Personal PM2.5-bound aluminum (Al) (113.41 ng/m3) showed the highest geometric mean (GM) concentration, followed by lead (Pb) (90.89 ng/m3), zinc (Zn) (67.71 ng/m3), and iron (Fe) (51.85 ng/m3). The elevated levels of PM2.5-bound Al, vanadium (V), manganese (Mn), arsenic (As), rubidium (Rb), cadmium (Cd), and thallium (Tl) were found in participants with cigarette smoke exposure, compared with those without. The concentrations of Rb and strontium (Sr) were positively associated with the time spent outdoors. The increased concentration of nickel (Ni) was found in individuals who spent > 30 min/day in traffic. The elevated levels of V, Mn, and cobalt (Co) were associated with a short distance from dwellings to the main road. The results of PCA showed that PM2.5-bound metals might come from five sources: As, selenium (Se), Rb, Cd, Tl, and Pb from cigarette smoke exposure; Al, V, Mn, Fe, and Sr from crustal dust; copper (Cu) and antimony (Sb) from industrial activities; Ni and Co from traffic emission; and Zn from coal combustion. The concentrations of PM2.5-bound metals in this study were at moderate levels. Cigarette smoke exposure, industrial activities, traffic emission, and coal combustion might be major anthropogenic sources of personal PM2.5-bound metal exposures in Wuhan, China.

Identification of High Personal PM2.5 Exposure during Real Time Commuting in the Taipei Metropolitan Area

There has been an increase in the network of mass rapid transit (MRT) and the number of automobiles over the past decades in the Taipei metropolitan area, Taiwan. The effects of these changes on PM2.5 exposure for the residents using different modes of transportation are unclear. Volunteers measured PM2.5 concentrations while commuting in different modes of transportation using a portable PM2.5 detector. Exposure to PM2.5 (median (range)) was higher when walking along the streets (40 (10–275) µg/m3) compared to riding the buses (35 (13–65) µg/m3) and the cars (15 (8–80) µg/m3). PM2.5 concentrations were higher in underground MRT stations (80 (30–210) µg/m3) and inside MRT cars running in underground sections (80 (55–185) µg/m3) than those in elevated MRT stations (33 (15–35) µg/m3) and inside MRT cars running in elevated sections (28 (13–68) µg/m3) (p < 0.0001). Riding motorcycle also was associated with high PM2.5 exposure (75 (60–105 µg/m3), p < 0.0001 vs. walking). High PM2.5 concentrations were noted near the temples (588 ± 271 µg/m3) and in the underground food court of a night market (405 ± 238 µg/m3) where the eatery stalls stir-fried and grilled food (p < 0.0001 vs. walking). We conclude that residents in the Taipei metropolitan area may still be exposed to high PM2.5 during some forms of commuting, including riding underground MRT.

Estimating ground-level PM2.5 levels in Taiwan using data from air quality monitoring stations and high coverage of microsensors

A widespread monitoring network of Airbox microsensors was implemented since 2016 to provide high-resolution spatial distributions of ground-level PM_2.5 data in Taiwan. We developed models for estimating ground-level PM_2.5 concentrations for all the 3 km × 3 km grids in Taiwan by combining the data from air quality monitoring stations and the Airbox sensors. The PM_2.5 data from the Airbox sensors (AB-PM_2.5) was used to predict daily mean PM_2.5 levels at the grids in 2017 using a semiparametric additive model. The estimated PM_2.5 level at the grids was further applied as a predictor variable in the models to predict the monthly mean concentration of PM_2.5 at all the grids in the previous year. The modeling-predicting procedures were repeated backward for the years from 2016 to 2006. The model results revealed that the model R² increased from 0.40 to 0.87 when the AB-PM_2.5 data were included as a nonlinear component in the model, indicating that AB-PM_2.5 is a significant predictor of ground-level PM_2.5 concentration. The cross-validation (CV) results demonstrated that the root of mean squared prediction errors of the estimated monthly mean PM_2.5 concentrations were smaller than 5 μg/m³ and the R² of the CV models of 0.79-0.88 during 2006-2017. We concluded that Airbox sensors can be used with monitoring data to more accurately estimate long-term exposure to PM_2.5 for cohorts of small areas in health impact assessment studies.

Brain lipid profiles in the spontaneously hypertensive rat after subchronic real-world exposure to ambient fine particulate matter

Recent studies have illustrated an association between ambient fine particulate matter (PM_2.5) exposure and neuronal toxicity in epidemiological studies and animal models. However, the possible molecular effects on brains under real-world exposure to PM_2.5 remain unclear. In this pilot study, male spontaneously hypertensive rats were whole-bodily exposed to ambient air from the outdoor environment of Taipei City for 3 months, while the control rats inhaled HEPA-filtered air. The PM_2.5-induced phosphatidylcholine and sphingomyelin profiles in the hippocampus, cortex, medulla, cerebellum, and olfactory bulb were assessed by mass spectrometry (MS)-based lipidomics. Partial least squares discriminant analysis (PLS-DA) and the Wilcoxon rank sum test were used to examine the lipid changes between the exposed and control groups. The PLS-DA models showed that phosphatidylcholine and sphingomyelin profiles of the PM_2.5 exposure group were different from those of the control group in each brain region except the cortex. More lipid changes were found in the hippocampus, while fewer lipid changes were observed in the olfactory bulb. The lipid alteration in the hippocampus may strengthen membrane integrity, modulate signaling pathways, and avoid accumulation of lipofuscin to counter the PM_2.5-induced stress. The lipid changes in the cortex and medulla may respond to PM_2.5-induced injury and inflammation; while the lipid changes in the cerebellum were associated with neuron protection. This study suggests that the MS-based lipidomics is a powerful approach to discriminate the brain lipid profiles even at the environmental level of ambient PM_2.5 and has the potential to suggest possible adverse health effects in long-term PM_2.5 exposure studies.

Modelling the vertical gradient of nitrogen dioxide in an urban area

INTRODUCTION

Land use regression models environmental predictors to estimate ground-floor air pollution concentration surfaces of a study area. While many cities are expanding vertically, such models typically ignore the vertical dimension.

METHODS

We took integrated measurements of NO₂ at up to three different floors on the facades of 25 buildings in the mid-sized European city of Basel, Switzerland. We quantified the decrease in NO₂ concentration with increasing height at each facade over two 14-day periods in different seasons. Using predictors of traffic load, population density and street configuration, we built conventional land use regression (LUR) models which predicted ground floor concentrations. We further evaluated which predictors best explained the vertical decay rate. Ultimately, we combined ground floor and decay models to explain the measured concentrations at all heights.

RESULTS

We found a clear decrease in mean nitrogen dioxide concentrations between measurements at ground level and those at higher floors for both seasons. The median concentration decrease was 8.1% at 10 m above street level in winter and 10.4% in summer. The decrease with height was sharper at buildings where high concentrations were measured on the ground and in canyon-like street configurations. While the conventional ground floor model was able to explain ground floor concentrations with a model R² of 0.84 (RMSE 4.1 μg/m³), it predicted measured concentrations at all heights with an R² of 0.79 (RMSE 4.5 μg/m³), systematically overpredicting concentrations at higher floors. The LUR model considering vertical decay was able to predict ground floor and higher floor concentrations with a model R² of 0.84 (RMSE 3.8 μg/m³) and without systematic bias.

DISCUSSION

Height above the ground is a relevant determinant of outdoor residential exposure, even in medium-sized European cities without much high-rise. It is likely that conventional LUR models overestimate exposure for residences at higher floors near major roads. This overestimation can be minimized by considering decay with height.

Premature Deaths Attributable to Long-term Exposure to Ambient Fine Particulate Matter in the Republic of Korea

BACKGROUND

Ambient fine particulate matter (PM_2.5) is the major environmental health risk factor in Korea. Exposure to PM_2.5 has been a growing public concern nationwide. With the rapid aging of the Korean population, the health effects attributable to long-term exposure to PM_2.5 were expected to increase further in the future. We aimed to estimate premature deaths attributable to long-term exposure to ambient PM_2.5 in Korea.

METHODS

A modelled estimation of long-term exposure to PM_2.5 was used to calculate the nationwide exposure level. Hazard ratios of long-term exposure to PM_2.5 were obtained from a large prospective cohort study in North America. Modified cause of death (CoD) data, which applied the garbage code reclassification algorithm, were used to calculate premature deaths attributable to long-term exposure to PM_2.5.

RESULTS

From 1990 to 2013, the average population-weighted PM_2.5 concentration in Korea was 30.2 μg/m³. The estimated number of premature deaths was 17,203 (95% confidence interval [CI], 11,056-22,772). The most common CoD was ischemic stroke (5,382; 3,101-7,403), followed by cancer of trachea, bronchus, and lung (4,958; 2,857-6,820), hemorrhagic stroke (3,452; 1,989-4,748), and ischemic heart disease (3,432; 1,383-5,358).

CONCLUSION

Premature deaths due to long-term exposure to PM_2.5 accounted for 6.4% of all deaths in Korea. However, individual efforts alone cannot prevent the effects of air pollution. This disease burden study can serve as a basis for the establishment of government policies and budgets and can be used to assess the effectiveness of environmental health policies.

Development of land-use regression models for fine particles and black carbon in peri-urban South India

Land-use regression (LUR) has been used to model local spatial variability of particulate matter in cities of high-income countries. Performance of LUR models is unknown in less urbanized areas of low-/middle-income countries (LMICs) experiencing complex sources of ambient air pollution and which typically have limited land use data. To address these concerns, we developed LUR models using satellite imagery (e.g., vegetation, urbanicity) and manually-collected data from a comprehensive built-environment survey (e.g., roads, industries, non-residential places) for a peri-urban area outside Hyderabad, India. As part of the CHAI (Cardiovascular Health effects of Air pollution in Telangana, India) project, concentrations of fine particulate matter (PM_2.5) and black carbon were measured over two seasons at 23 sites. Annual mean (sd) was 34.1 (3.2) μg/m³ for PM_2.5 and 2.7 (0.5) μg/m³ for black carbon. The LUR model for annual black carbon explained 78% of total variance and included both local-scale (energy supply places) and regional-scale (roads) predictors. Explained variance was 58% for annual PM_2.5 and the included predictors were only regional (urbanicity, vegetation). During leave-one-out cross-validation and cross-holdout validation, only the black carbon model showed consistent performance. The LUR model for black carbon explained a substantial proportion of the spatial variability that could not be captured by simpler interpolation technique (ordinary kriging). This is the first study to develop a LUR model for ambient concentrations of PM_2.5 and black carbon in a non-urban area of LMICs, supporting the applicability of the LUR approach in such settings. Our results provide insights on the added value of manually-collected built-environment data to improve the performance of LUR models in settings with limited data availability. For both pollutants, LUR models predicted substantial within-village variability, an important feature for future epidemiological studies.

Methods for Assessing Long-Term Exposures to Outdoor Air Pollutants

Purpose of Review

Epidemiological studies of health effects of long-term exposure to outdoor air pollution rely on different exposure assessment methods. This review discusses widely used methods with a special focus on new developments.

Recent Findings

New data and study designs have been applied, including satellite measurements of fine particles and nitrogen dioxide (NO₂). The methods to apply satellite data for epidemiological studies are improving rapidly and have already contributed significantly to national-, continental- and global-scale models. Spatiotemporal models have been developed allowing more detailed temporal resolution compared to spatial models. The development of hybrid models combining dispersion models, satellite observations, land use and surface monitoring has improved models substantially. Mobile monitoring designs to develop models for long-term UFP exposure have been conducted.

Summary

Methods to assess long-term exposure to outdoor air pollution have improved significantly over the past decade. Application of satellite data and mobile monitoring designs is promising new methods.

Source apportionment of urban air pollutants using constrained receptor models with a priori profile information

Exposure to air pollutants such as volatile organic compounds (VOCs) and fine particulate matter (PM_2.5) are associated with adverse health effects. This study applied multiple time resolution data of hourly VOCs and 24-h PM_2.5 to a constrained Positive Matrix Factorization (PMF) model for source apportionment in Taipei, Taiwan. Ninety-two daily PM_2.5 samples and 2208 hourly VOC measurements were collected during four seasons in 2014 and 2015. With some a priori information, we used different procedures to constrain retrieved factors toward realistic sources. A total of nine source factors were identified as: natural gas/liquefied petroleum gas (LPG) leakage, solvent use/industrial process, contaminated marine aerosol, secondary aerosol/long-range transport, oil combustion, traffic related, evaporative gasoline emission, gasoline exhaust, and soil dust. Results showed that solvent use/industrial process was the largest contributor (19%) to VOCs while the largest contributor to PM_2.5 mass was secondary aerosol/long-range transport (57%). A robust regression analysis showed that secondary aerosol was mostly contributed by regional transport related factor (25%).

Fine Particulate Matter Concentrations in Urban Chinese Cities, 2005-2016: A Systematic Review

Background: Particulate matter pollution has become a growing health concern over the past few decades globally. The problem is especially evident in China, where particulate matter levels prior to 2013 are publically unavailable. We conducted a systematic review of scientific literature that reported fine particulate matter (PM_2.5) concentrations in different regions of China from 2005 to 2016. Methods: We searched for English articles in PubMed and Embase and for Chinese articles in the China National Knowledge Infrastructure (CNKI). We evaluated the studies overall and categorized the collected data into six geographical regions and three economic regions. Results: The mean (SD) PM_2.5 concentration, weighted by the number of sampling days, was 60.64 (33.27) μg/m³ for all geographic regions and 71.99 (30.20) μg/m³ for all economic regions. A one-way ANOVA shows statistically significant differences in PM_2.5 concentrations between the various geographic regions (F = 14.91, p < 0.0001) and the three economic regions (F = 4.55, p = 0.01). Conclusions: This review identifies quantifiable differences in fine particulate matter concentrations across regions of China. The highest levels of fine particulate matter were found in the northern and northwestern regions and especially Beijing. The high percentage of data points exceeding current federal regulation standards suggests that fine particulate matter pollution remains a huge problem for China. As pre-2013 emissions data remain largely unavailable, we hope that the data aggregated from this systematic review can be incorporated into current and future models for more accurate historical PM_2.5 estimates.

Comparison of trace elements in size-fractionated particles in two communities with contrasting socioeconomic status in Houston, TX

Levels of ambient air pollutants, including particulate matter (PM), are often higher in low-socioeconomic status (SES) communities than in high-SES communities. Houston is the fourth largest city in the USA and is home to a large petrochemical industry, an active port, and congested roadways, which represent significant emission sources of air pollution in the region. To compare levels of air pollution between a low-SES and a high-SES community, we simultaneously collected a 7-day integrated size-fractionated PM between June 2013 and November 2013. We analyzed PM mass and elements for three particle size modes: quasi-ultrafine particles (quasi-UFP) (aerodynamic diameter <0.25 μm), accumulation mode particles (0.25-2.5 μm), and coarse mode particles (>2.5 μm). Concentrations of vanadium, nickel, manganese, and iron in the quasi-UFP mode were significantly higher in the low-SES community than in the high-SES community. In the accumulation and coarse modes, concentrations of crustal elements and barium were also significantly higher in the low-SES community compared to the high-SES community. These findings suggest that people living in the low-SES community may experience higher exposures to some toxic elements as compared to people in the high-SES community.

Assessment of different route choice on commuters' exposure to air pollution in Taipei, Taiwan

The purposes of this study are to develop a healthy commute map indicating cleanest route in Taipei metropolitan area for any given journey and to evaluate the pollutant doses exposed in different commuting modes. In Taiwan, there are more than 13.6 million motorcycles and 7.7 million vehicles among the 23 million people. Exposure to traffic-related air pollutants can thus cause adverse health effects. Moreover, increasing the level of physical activity during commuting and longer distances will result in inhalation of more polluted air. In this study, we utilized air pollution monitoring data (CO, SO₂, NO₂, PM₁₀, and PM_2.5) from Taiwan EPA's air quality monitoring stations in Taipei metropolitan area to estimate each pollutant exposure while commuting by different modes (motorcycling, bicycling, and walking). Spatial interpolation methods such as inverse distance weighting (IDW) were used to estimate each pollutant's distribution in Taipei metropolitan area. Three routes were selected to represent the variety of different daily commuting pathways. The cleanest route choice was based upon Dijkstra's algorithm to find the lowest cumulative pollutant exposure. The IDW interpolated values of CO, SO₂, NO₂, PM₁₀, and PM_2.5 ranged from 0.42-2.2 (ppm), 2.6-4.8 (ppb), 17.8-42.9 (ppb), 32.4-65.6 (μg/m³), and 14.2-38.9 (μg/m³), respectively. To compare with the IDW results, concentration of particulate matter (PM₁₀, PM_2.5, and PM₁) along the motorcycle route was measured in real time. In conclusion, the results showed that the shortest commuting route for motorcyclists resulted in a much higher cumulative dose (PM_2.5 3340.8 μg/m³) than the cleanest route (PM_2.5 912.5 μg/m³). The mobile personal monitoring indicated that the motorcyclists inhaled significant high pollutants during commuting as a result of high-concentration exposure and short-duration peaks. The study could effectively present less polluted commuting routes for citizen health benefits.

The spatiotemporal distributions and determinants of ambient fungal spores in the Greater Taipei area

Airborne fungal spores, a type of bioaerosols, are significant air pollutants. We conducted a study to determine the spatiotemporal distributions of ambient fungi in the Greater Taipei area and develop land use regression (LUR) models for total and major fungal taxa. Four seasonal sampling campaigns were conducted over a year at 44 representative sites. Multiple regressions were performed to construct the LUR models. Ascospores were the most prevalent category, followed by Aspergillus/Penicillium, basidiospores, and Cladosporium. The highest fungal concentrations were found in spring. According to the LUR models, higher concentrations of Aspergillus/Penicillium and basidiospores were respectively present in residential/commercial areas and in areas with shorter road lengths. Various meteorological factors, particulates with aerodynamic diameters of ≤10 μm, and elevation also had significant relationships with fungal concentrations. The LUR models developed in this study can be used to assess spatiotemporal fungal distribution in the Greater Taipei area.

Associations between Long-Term Air Pollutant Exposures and Blood Pressure in Elderly Residents of Taipei City: A Cross-Sectional Study

BACKGROUND

Limited information is available regarding long-term effects of air pollution on blood pressure (BP) and hypertension.

OBJECTIVE

We studied whether 1-year exposures to particulate matter (PM) and nitrogen oxides (NOx) were correlated with BP and hypertension in the elderly.

METHODS

We analyzed cross-sectional data from 27,752 Taipei City residents > 65 years of age who participated in a health examination program in 2009. Land-use regression models were used to estimate participants' 1-year exposures to particulate matter with aerodynamic diameter ≤ 10 μm (PM10), coarse particles (PM2.5-10), fine particles (≤ 2.5 μm; PM2.5), PM2.5 absorbance, NOx, and nitrogen dioxide (NO2). Generalized linear regressions and logistic regressions were used to examine the association between air pollution and BP and hypertension, respectively.

RESULTS

Diastolic BP was associated with 1-year exposures to air pollution, with estimates of 0.73 [95% confidence interval (CI): 0.44, 1.03], 0.46 (95% CI: 0.30, 0.63), 0.62 (95% CI: 0.24, 0.99), 0.34 (95% CI: 0.19, 0.50), and 0.65 (95% CI: 0.44, 0.85) mmHg for PM10 (10 μg/m3), PM2.5-10 (5 μg/m3), PM2.5 absorbance (10-5/m), NOx (20 μg/m3), and NO2 (10 μg/m3), respectively. PM2.5 was not associated with diastolic BP, and none of the air pollutants was associated with systolic BP. Associations of diastolic BP with PM10 and PM2.5 absorbance were stronger among participants with hypertension, diabetes, or a body mass index ≥ 25 kg/m2 than among participants without these conditions. One-year air pollution exposures were not associated with hypertension.

CONCLUSIONS

One-year exposures to PM10, PM2.5-10, PM2.5 absorbance, and NOx were associated with higher diastolic BP in elderly residents of Taipei.

Land use regression models for crustal and traffic-related PM2.5 constituents in four areas of the SAPALDIA study

Many studies have documented adverse health effects of long-term exposure to fine particulate matter (PM2.5), but there is still limited knowledge regarding the causal relationship between specific sources of PM2.5 and such health effects. The spatial variability of PM2.5 constituents and sources, as a exposure assessment strategy for investigating source contributions to health effects, has been little explored so far. Between 2011 and 2012, three measurement campaigns of PM and nitrogen dioxide (NO2) were performed in 80 sites across four areas of the Swiss Study on Air Pollution and Lung and heart Diseases in Adults (SAPALDIA). Reflectance analysis and energy dispersive X-ray fluorescence (XRF) were performed on PM2.5 filter samples to estimate light absorbance and trace element concentrations, respectively. Three air pollution source factors were identified using principal-component factor analysis: vehicular, crustal, and long-range transport. Land use regression (LUR) models were developed for temporally-adjusted scores of each factor, combining the four study areas. Model performance was assessed using two cross-validation methods. Model explained variance was high for the vehicular factor (R(2)=0.76), moderate for the crustal factor (R(2)=0.46), and low for the long-range transport factor (R(2)=0.19). The cross-validation methods suggested that models for the vehicular and crustal factors moderately accounted for both the between and within-area variability, and therefore can be applied to the four study areas to estimate long-term exposures within the SAPALDIA study population. The combination of source apportionment techniques and LUR modelling may help in identifying air pollution sources and disentangling their contribution to observed health effects in epidemiologic studies.

Applying land use regression model to estimate spatial variation of PM₂.₅ in Beijing, China

Fine particulate matter (PM2.5) is the major air pollutant in Beijing, posing serious threats to human health. Land use regression (LUR) has been widely used in predicting spatiotemporal variation of ambient air-pollutant concentrations, though restricted to the European and North American context. We aimed to estimate spatiotemporal variations of PM2.5 by building separate LUR models in Beijing. Hourly routine PM2.5 measurements were collected at 35 sites from 4th March 2013 to 5th March 2014. Seventy-seven predictor variables were generated in GIS, including street network, land cover, population density, catering services distribution, bus stop density, intersection density, and others. Eight LUR models were developed on annual, seasonal, peak/non-peak, and incremental concentration subsets. The annual mean concentration across all sites is 90.7 μg/m(3) (SD = 13.7). PM2.5 shows more temporal variation than spatial variation, indicating the necessity of building different models to capture spatiotemporal trends. The adjusted R (2) of these models range between 0.43 and 0.65. Most LUR models are driven by significant predictors including major road length, vegetation, and water land use. Annual outdoor exposure in Beijing is as high as 96.5 μg/m(3). This is among the first LUR studies implemented in a seriously air-polluted Chinese context, which generally produce acceptable results and reliable spatial air-pollution maps. Apart from the models for winter and incremental concentration, LUR models are driven by similar variables, suggesting that the spatial variations of PM2.5 remain steady for most of the time. Temporal variations are explained by the intercepts, and spatial variations in the measurements determine the strength of variable coefficients in our models.