Estimation of fine particulate matter in Taipei using landuse regression and bayesian maximum entropy methods

Assessing the ecological risk induced by PM2.5 pollution in a fast developing urban agglomeration of southeastern China

High PM_2.5 concentration threats ecosystem functions but limited quantitative studies have recognized PM_2.5 pollution as an individual stressor in evaluating ecological risk. In this study, we applied a machine-learning-based simulation model incorporating full-coverage satellite-driven PM_2.5 dataset to estimate high-resolution ground PM_2.5 concentration for the Golden Triangle of Southern Fujian Province, China (GTSF) in 2030 under two Representative Concentration Pathways (RCPs). Based on the simulation output, the ecological risk's spatiotemporal change and the risk for different land cover types, which were caused by PM_2.5 pollution, were assessed. We found that the PM_2.5 levels and ecological risk in the GTSF under RCP 4.5 would be reduced while those under RCP 8.5 would continue to increase. The regions with the highest ecological risk under RCP 4.5 are the most urbanized and industrialized districts, while those with the highest ecological risk under RCP 8.5 are of the highest rate in urbanization and the greatest decrease in planetary potential layer height. For both base years and 2030 under two RCPs, the ecological risk on developed land is the highest, while that on the forest is the lowest. Our study can provide useful information for environmental policy risk assessment.

Long-Term Exposure to Air Pollutants and Cognitive Function in Taiwanese Community-Dwelling Older Adults: A Four-Year Cohort Study

BACKGROUND

Previous studies have assessed limited cognitive domains with relatively short exposure to air pollutants, and studies in Asia are limited.

OBJECTIVE

This study aims to explore the association between long-term exposure to air pollutants and cognition in community-dwelling older adults.

METHODS

This four-year prospective cohort study recruited 605 older adults at baseline (2011-2013) and 360 participants remained at four-year follow-up. Global and domain-specific cognition were assessed biennially. Data on PM2.5 (particulate matter≤2.5μm diameter, 2005-2015), PM10 (1993-2015), and nitrogen dioxide (NO2, 1993-2015) were obtained from Taiwan Environmental Protection Administration (TEPA). Bayesian Maximum Entropy was utilized to estimate the spatiotemporal distribution of levels of these pollutants.

RESULTS

Exposure to high-level PM2.5 (>29.98μg/m3) was associated with an increased risk of global cognitive impairment (adjusted odds ratio = 4.56; β= -0.60). High-level PMcoarse exposure (>26.50μg/m3) was associated with poor verbal fluency (β= -0.19). High-level PM10 exposure (>51.20μg/m3) was associated with poor executive function (β= -0.24). Medium-level NO2 exposure (>28.62 ppb) was associated with better verbal fluency (β= 0.12). Co-exposure to high concentrations of PM2.5, PMcoarse or PM10 and high concentration of NO2 were associated with poor verbal fluency (PM2.5 and NO2: β= -0.17; PMcoarse and NO2: β= -0.23; PM10 and NO2: β= -0.21) and poor executive function (PM10 and NO2: β= -0.16). These associations became more evident in women, apolipoprotein ɛ4 non-carriers, and those with education > 12 years.

CONCLUSION

Long-term exposure to PM2.5 (higher than TEPA guidelines), PM10 (lower than TEPA guidelines) or co-exposure to PMx and NO2 were associated with poor global, verbal fluency, and executive function over 4 years.

Development of a noise-assisted multivariate empirical mode decomposition framework for characterizing PM 2.5 air pollution in Taiwan and its relation to hydro-meteorological factors

To better understand air pollution problems, the relationships between PM_2.5 and hydro-meteorological variables are studied using a state-of-the-art multivariate nonlinear and non-stationary filtering method, noise-assisted multivariate empirical mode decomposition (NAMEMD), and the time-dependent intrinsic correlation (TDIC) algorithm. Three characteristic scales (annual, diurnal and semi-diurnal) are shown to be significant to PM2.5 characterization, based on using NAMEMD filtering. Temporal fluctuations of local correlations among PM_2.5 and hydro-meteorological variables are presented. On diurnal and semi-diurnal scales, seasonal variation of the local correlation between temperature and humidity is observed. A combined wind speed and direction analysis can be conducted using the NAMEMD-based algorithm. The pollutant roses that are generated from the reconstructed wind directions reveal the sources of PM_2.5 on different scales. PM_2.5 is found to be related to land breeze at the diurnal scale and to winter monsoons at the annual scale. The scale-dependent wind direction that contributes to the increase of PM_2.5 can be identified.

Land Use Impacts on Particulate Matter Levels in Seoul, South Korea: Comparing High and Low Seasons

Seoul, a city in South Korea, experiences high particulate matter (PM) levels well above the recommended standards suggested by the World Health Organization. As concerns about public health and everyday lives are being raised, this study investigates the effects of land use on PM levels in Seoul. Specifically, it attempts to identify which land use types increase or decrease PM10 and PM2.5 levels and compare the effects between high and low seasons using two sets of land use classifications: one coarser and the other finer. A series of partial least regression models identifies that industrial land use increases the PM levels in all cases. It is also reported that residential and commercial land uses associated with lower density increase these levels. Other uses, such as green spaces and road, show mixed or unclear effects. The findings of this study may inform planners and policymakers about how they can refine future land use planning and development practice in cities that face similar challenges.

Assessment of the spatio-temporal pattern of PM2.5 and its driving factors using a land use regression model in Beijing, China

With the acceleration of urbanization and industrialization, atmospheric pollution has become a major issue, restricting the sustainable development of the urban environment. Since 2013, Beijing has been among China's most seriously affected regions in terms of haze pollution. Atmospheric pollution is closely linked to land use, particularly the spatial patterns of green and urban land. Therefore, the quantification of the relationship between fine particulate matter (PM_2.5) concentration and its driving factors in Beijing is of considerable significance for environmental management and spatial epidemiological studies. A land use regression (LUR) model was constructed to simulate the spatio-temporal distribution of PM_2.5 concentration. In this study, the independent variables (driving factors) included land use, meteorological factors, population, roads, the digital elevation model, and the normalized difference vegetation index. The five models had adjusted R² of 0.887, 0.770, 0.742, 0.877, and 0.798, respectively. Land use and meteorological factors were the main factors affecting PM_2.5 concentration. The driving factors of land use on a large scale and roads on a small scale had a significant impact on PM_2.5 emissions. Beijing's PM_2.5 concentrations in 2015 showed clear spatio-temporal characteristics. The highest (lowest) average PM_2.5 concentration was recorded in winter (summer). In terms of spatial distribution, PM_2.5 concentrations showed a "low in the northwest and high in the southeast" trend. The most polluted areas were mainly distributed in the central city and the southeastern and southwestern regions. The PM_2.5 concentration boundary was essentially consistent with the boundary of land use type. Different land use types promoted or inhibited PM_2.5 concentrations, with a difference of more than 20 μg/m³ PM_2.5 between the two land use categories. Thus, PM_2.5 concentrations should be controlled by optimizing the spatial and temporal patterns of land use.

Distribution of PM2.5 Air Pollution in Mexico City: Spatial Analysis with Land-Use Regression Model

In this study, the spatial distribution of PM2.5 air pollution in Mexico City from 37 personal exposures was modeled. Meteorological, demographic, geographic, and social data were also included. Geographic information systems (GIS), spatial analysis, and Land-Use Regression (LUR) were used to generate the final predictive model and the spatial distribution map which revealed two areas with very high concentrations (up to 109.3 µg/m3) and two more with lower concentrations (between 72 to 86.5 µg/m3) (p < 0.05). These results illustrate an overview trend of PM2.5 in relation to human activity during the studied periods in Mexico City and show a general approach to understanding the spatial variability of PM2.5.

Space-time PM2.5 mapping in the severe haze region of Jing-Jin-Ji (China) using a synthetic approach

Long- and short-term exposure to PM_2.5 is of great concern in China due to its adverse population health effects. Characteristic of the severity of the situation in China is that in the Jing-Jin-Ji region considered in this work a total of 2725 excess deaths have been attributed to short-term PM_2.5 exposure during the period January 10-31, 2013. Technically, the processing of large space-time PM_2.5 datasets and the mapping of the space-time distribution of PM_2.5 concentrations often constitute high-cost projects. To address this situation, we propose a synthetic modeling framework based on the integration of (a) the Bayesian maximum entropy method that assimilates auxiliary information from land-use regression and artificial neural network (ANN) model outputs based on PM_2.5 monitoring, satellite remote sensing data, land use and geographical records, with (b) a space-time projection technique that transforms the PM_2.5 concentration values from the original spatiotemporal domain onto a spatial domain that moves along the direction of the PM_2.5 velocity spread. An interesting methodological feature of the synthetic approach is that its components (methods or models) are complementary, i.e., one component can compensate for the occasional limitations of another component. Insight is gained in terms of a PM_2.5 case study covering the severe haze Jing-Jin-Ji region during October 1-31, 2015. The proposed synthetic approach explicitly accounted for physical space-time dependencies of the PM_2.5 distribution. Moreover, the assimilation of auxiliary information and the dimensionality reduction achieved by the synthetic approach produced rather impressive results: It generated PM_2.5 concentration maps with low estimation uncertainty (even at counties and villages far away from the monitoring stations, whereas during the haze periods the uncertainty reduction was over 50% compared to standard PM_2.5 mapping techniques); and it also proved to be computationally very efficient (the reduction in computational time was over 20% compared to standard mapping techniques).

Air Pollution Monitoring Design for Epidemiological Application in a Densely Populated City

Introduction: Many studies have reported the association between air pollution and human health based on regulatory air pollution monitoring data. However, because regulatory monitoring networks were not designed for epidemiological studies, the collected data may not provide sufficient spatial contrasts for assessing such associations. Our goal was to develop a monitoring design supplementary to the regulatory monitoring network in Seoul, Korea. This design focused on the selection of 20 new monitoring sites to represent the variability in PM_2.5 across people’s residences for cohort studies. Methods: We obtained hourly measurements of PM_2.5 at 37 regulatory monitoring sites in 2010 in Seoul, and computed the annual average at each site. We also computed 313 geographic variables representing various pollution sources at the regulatory monitoring sites, 31,097 children’s homes from the Atopy Free School survey, and 412 community service centers in Seoul. These three types of locations represented current, subject, and candidate locations. Using the regulatory monitoring data, we performed forward variable selection and chose five variables most related to PM_2.5. Then, k-means clustering was applied to categorize all locations into several groups representing a diversity in the spatial variability of the five selected variables. Finally, we computed the proportion of current to subject location in each cluster, and randomly selected new monitoring sites from candidate sites in the cluster with the minimum proportion until 20 sites were selected. Results: The five selected geographic variables were related to traffic or urbanicity with a cross-validated R² value of 0.69. Clustering analysis categorized all locations into nine clusters. Finally, one to eight new monitoring sites were selected from five clusters. Discussion: The proposed monitoring design will help future studies determine the locations of new monitoring sites representing spatial variability across residences for epidemiological analyses.

A land use regression application into assessing spatial variation of intra-urban fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations in City of Shanghai, China

Intra-urban assessment of air pollution exposure has become a priority study while international attention was attracted to PM2.5 pollution in China in recent years. Land Use Regression (LUR), which has previously been proved to be a feasible way to describe the relationship between land use and air pollution level in European and American cities, was employed in this paper to explain the correlations and spatial variations in Shanghai, China. PM2.5 and NO2 concentrations at 35-45 monitoring locations were selected as dependent variables, and a total of 44 built environmental factors were extracted as independent variables. Only five factors showed significant explanatory value for both PM2.5 and NO2 models: longitude, distance from monitors to the ocean, highway intensity, waterbody area, and industrial land area for PM2.5 model; residential area, distance to the coast, industrial area, urban district, and highway intensity for NO2 model. Respectively, both PM2.5 and NO2 showed anti-correlation with coastal proximity (an indicator of clean air dilution) and correlation with highway and industrial intensity (source indicators). NO2 also showed significant correlation with local indicators of population density (residential intensity and urban classification), while PM2.5 showed significant correlation with regional dilution (longitude as a indicator of distance from polluted neighbors and local water features). Both adjusted R squared values were strong with PM2.5 (0.88) being higher than NO2 (0.62). The LUR was then used to produce continuous concentration fields for NO2 and PM2.5 to illustrate the features and, potentially, for use by future studies. Comparison to PM2.5 studies in New York and Beijing show that Shanghai PM2.5 pollutant distribution was more sensitive to geographic location and proximity to neighboring regions.

Spatiotemporal analysis of ambient air pollution exposure and respiratory infections cases in Beijing

BACKGROUND

Ambient air pollutants (PM2.5) are components of persistent haze in Beijing during the autumn and winter seasons.

MATERIALS

We collected hourly PM2.5 monitoring data for 35 days from 35 sites in Beijing during 2012. We also identified patients developing respiratory infections during the same time period in the same locale. A BME model was used to simulate environmental exposure concentrations over the course of each day. A medical accessibility analysis was performed to exclude the impact of medical availability on the analysis. A spatial analysis was included in the evaluation of the relationship between exposure duration and concentration of PM2.5 with the development of acute respiratory disease.

RESULTS

A low concentration of PM2.5 (greater than 35 µg/m3 and less than 115 µg/m3) for at least 3 days was associated with an increased risk of acute respiratory disease. A high concentration of PM2.5 (greater than 115 µg/m3) was associated with an increased risk of infection even after 1 day of exposure.

Association between air pollutants and dementia risk in the elderly

Background

The aging rate in Taiwan is the second highest in the world. As the population ages quickly, the prevalence of dementia increases rapidly. There are some studies that have explored the association between air pollution and cognitive decline, but the association between air pollution and dementia has not been directly evaluated.

Methods

This was a case-control study comprising 249 Alzheimer's disease (AD) patients, 125 vascular dementia (VaD) patients, and 497 controls from three teaching hospitals in northern Taiwan from 2007 to 2010. Data of particulate matter <10 μm in diameter (PM₁₀) and ozone were obtained from the Taiwan Environmental Protection Administration for 12 and 14 years, respectively. Blood samples were collected to determine the apolipoprotein E (APOE) ɛ4 haplotype. Bayesian maximum entropy was used to estimate the individual exposure level of air pollutants, which was then tertiled for analysis. Conditional logistic regression models were used to estimate adjusted odds ratios (AORs) and 95% confidence intervals between the association of PM₁₀ and ozone exposure with AD and VaD risk.

Results

The highest tertile of PM₁₀ (≥49.23 μg/m³) or ozone (≥21.56 ppb) exposure was associated with increased AD risk (highest vs. lowest tertile of PM₁₀: AOR = 4.17; highest vs. lowest tertile of ozone: AOR = 2.00). Similar finding was observed for VaD. The association with AD and VaD risk remained for the highest tertile PM₁₀ exposure after stratification by APOE ɛ4 status and gender.

Conclusions

Long-term exposure to the highest tertile of PM₁₀ or ozone was significantly associated with an increased risk of AD and VaD.

An LUR/BME framework to estimate PM2.5 explained by on road mobile and stationary sources

Knowledge of particulate matter concentrations <2.5 μm in diameter (PM2.5) across the United States is limited due to sparse monitoring across space and time. Epidemiological studies need accurate exposure estimates in order to properly investigate potential morbidity and mortality. Previous works have used geostatistics and land use regression (LUR) separately to quantify exposure. This work combines both methods by incorporating a large area variability LUR model that accounts for on road mobile emissions and stationary source emissions along with data that take into account incompleteness of PM2.5 monitors into the modern geostatistical Bayesian Maximum Entropy (BME) framework to estimate PM2.5 across the United States from 1999 to 2009. A cross-validation was done to determine the improvement of the estimate due to the LUR incorporation into BME. These results were applied to known diseases to determine predicted mortality coming from total PM2.5 as well as PM2.5 explained by major contributing sources. This method showed a mean squared error reduction of over 21.89% oversimple kriging. PM2.5 explained by on road mobile emissions and stationary emissions contributed to nearly 568,090 and 306,316 deaths, respectively, across the United States from 1999 to 2007.

Spatial vulnerability under extreme events: a case of Asian dust storm's effects on children's respiratory health

Asian dust storm (ADS) events have raised concerns regarding their adverse impact on human health. Whether ADS events can result in the heterogeneity of health impacts on children across space and time has not been studied. The goal of this study is to examine the spatial vulnerability impact of ADS events on children's respiratory health geographically and to analyze any patterns related to ADS episodes. From 1998 to 2007, data from both preschool children's and schoolchildren's daily respiratory clinic visits, gathered from patients located in 41 districts of Taipei City and New Taipei City, are analyzed in a Bayesian spatiotemporal model in order to investigate the interaction between spatial effects and ADS episodes. When adjusting for the temporal effect, air pollutants, and temperature, the spatial pattern explicitly varies during defined study periods: non-ADS periods, ADS periods, and post-ADS periods. Compared to non-ADS periods, the relative rate of children's respiratory clinic visits significantly reduced 0.74 to 0.99 times in most districts during ADS periods, while the relative rate rose from 1.01 to 1.11 times in more than half of districts during post-ADS periods, especially in schoolchildren. This spatial vulnerability denotes that the significantly increased relative rate of respiratory clinic visits during post-ADS periods is primarily located in highly urbanized areas for both children's populations. Hence, the results of this study suggest that schoolchildren are particularly more vulnerable to the health impacts of ADS exposure in terms of higher excessive risks over a larger spatial extent than preschool children, especially during post-ADS periods.

Quantile-based Bayesian maximum entropy approach for spatiotemporal modeling of ambient air quality levels

Understanding the daily changes in ambient air quality concentrations is important to the assessing human exposure and environmental health. However, the fine temporal scales (e.g., hourly) involved in this assessment often lead to high variability in air quality concentrations. This is because of the complex short-term physical and chemical mechanisms among the pollutants. Consequently, high heterogeneity is usually present in not only the averaged pollution levels, but also the intraday variance levels of the daily observations of ambient concentration across space and time. This characteristic decreases the estimation performance of common techniques. This study proposes a novel quantile-based Bayesian maximum entropy (QBME) method to account for the nonstationary and nonhomogeneous characteristics of ambient air pollution dynamics. The QBME method characterizes the spatiotemporal dependence among the ambient air quality levels based on their location-specific quantiles and accounts for spatiotemporal variations using a local weighted smoothing technique. The epistemic framework of the QBME method can allow researchers to further consider the uncertainty of space-time observations. This study presents the spatiotemporal modeling of daily CO and PM10 concentrations across Taiwan from 1998 to 2009 using the QBME method. Results show that the QBME method can effectively improve estimation accuracy in terms of lower mean absolute errors and standard deviations over space and time, especially for pollutants with strong nonhomogeneous variances across space. In addition, the epistemic framework can allow researchers to assimilate the site-specific secondary information where the observations are absent because of the common preferential sampling issues of environmental data. The proposed QBME method provides a practical and powerful framework for the spatiotemporal modeling of ambient pollutants.