Patterns Of PM2 Research Articles

Differentiating local and regional drivers of exceedances of WHO PM2.5 standards with a low-cost sensor network in the greater male’ region (GMR)

Although pollution is visible at certain times in the capital Greater Male’ Region (GMR), Maldives, only limited quantitative measures of the pollution concentrations are available. Hence, this study aims to understand the spatiotemporal patterns of Particulate Matter 2.5 (PM2.5) and its contribution by both local and regional sources. Data was collected using harmonized and calibrated low-cost PM sensors from October 2021 to September 2023 across 8 locations in GMR. Results showed that the annual average PM2.5 concentration in both years surpassed the WHO guideline. Higher levels of PM2.5 were measured in the dry season and the 24-h WHO-recommended level was surpassed in 67% of the days in the dry season. This is the effect of pollutants transported from the subcontinental regions. PM2.5 concentrations in at least 93% of the days in the wet season (Apr–Oct) remained within the daily limit. Similar levels and patterns of PM2.5 concentrations were noticed across all locations, despite higher concentrations and more frequent exceedances were observed in certain locations in GMR. Besides vehicular emission, electricity generation, biomass burning, sea transport and construction activities are the likely local sources of PM2.5. The diurnal trend revealed significant peaks on weekend evenings in some hotspots due to increased outdoor activities. Hence to reduce regional drivers of pollutants, corporations from countries in the region must take action and municipalities need to implement plans to moderate local emissions.

Daytime and night-time patterns in PM2.5 concentrations over Palembang City of Indonesia using MERRA-2 reanalysis data

ABSTRACT Palembang has sustained heavy haze pollution events that are related to decrease public health quality. Because ground-based PM2.5 observation in the city has some limitations, such as spatial and temporal coverages. Thus, in this study, we applied the potential of using the surface mass concentration PM2.5 data acquired by the MERRA-2 satellite to predict daytime and night-time PM2.5 levels during heavy haze events in Palembang City. Results of the study revealed that the highest PM2.5 level was about 160 µg/m3 at 02:00 (night-time), while the lowest PM2.5 level was about 50 µg/m3 at 14:00 (daytime). Spatiotemporal variation of PM2.5 over the study area showed that more than half of the area showed PM2.5 above 90 µg/m3 at night. Stable wind conditions (2 m/s) and air temperature (29°C), and relative humidity (74%) in the study area have contributed to particulate matter accumulation. The study concluded that PM2.5 acquired by the MERRA-2 satellite could provide a new reliable source for assessing severe air pollution events.

Geospatial Modelling for Estimation of PM2.5 Concentrations in Two Megacities in Peninsular India

Airborne particles finer than 2.5 microns (PM2.5) constitute a major public health risk in India. Therefore, extensive scientific studies must be conducted to assess the PM2.5 exposures of Indians and determine the “exposure-response function” specific to India. While Peninsular India includes three megacities with populations exceeding 10 million each, there are very few studies on air quality modelling in this region compared to North India. In this paper, the authors describe a Linear Mixed Effects (LME) model to estimate monthly-average PM2.5 concentrations at a spatial resolution of 1 km2 between 2016 and 2019 in the megacities of Bengaluru and Hyderabad with a total population of 23 million. This model is based on covariates such as aerosol optical depth (AOD), meteorological parameters, and Land-use-Land-cover (LULC) variables and is validated with extensive datasets from continuous and manual air quality monitoring stations through a 10-fold cross-validation process. The final LME model can explain more than 60 percent of the variation in the PM2.5 concentrations in Bengaluru and Hyderabad. This model is then used to predict the monthly-average grid-wise PM2.5 concentrations in more than 800 grids in each of these two cities to study the spatial and temporal patterns in PM2.5 concentrations between 2016 and 2019. These spatiotemporal maps of PM2.5 concentration are critical to overcoming the misclassification of exposure and will form a crucial input to much-needed PM exposure-response studies in these two megacities. This paper can serve as a useful framework for similar studies by showing the way to bridge the gaps in the current air quality monitoring network in Peninsular India.

Using dynamic time warping self-organizing maps to characterize diurnal patterns in environmental exposures

Advances in measurement technology are producing increasingly time-resolved environmental exposure data. We aim to gain new insights into exposures and their potential health impacts by moving beyond simple summary statistics (e.g., means, maxima) to characterize more detailed features of high-frequency time series data. This study proposes a novel variant of the Self-Organizing Map (SOM) algorithm called Dynamic Time Warping Self-Organizing Map (DTW-SOM) for unsupervised pattern discovery in time series. This algorithm uses DTW, a similarity measure that optimally aligns interior patterns of sequential data, both as the similarity measure and training guide of the neural network. We applied DTW-SOM to a panel study monitoring indoor and outdoor residential temperature and particulate matter air pollution (PM2.5) for 10 patients with asthma from 7 households near Salt Lake City, UT; the patients were followed for up to 373 days each. Compared to previous SOM algorithms using timestamp alignment on time series data, the DTW-SOM algorithm produced fewer quantization errors and more detailed diurnal patterns. DTW-SOM identified the expected typical diurnal patterns in outdoor temperature which varied by season, as well diurnal patterns in PM2.5 which may be related to daily asthma outcomes. In summary, DTW-SOM is an innovative feature engineering method that can be applied to highly time-resolved environmental exposures assessed by sensors to identify typical diurnal (or hourly or monthly) patterns and provide new insights into the health effects of environmental exposures.

Spatio-temporal modeling of PM2.5 risk mapping using three machine learning algorithms

Urban air pollution is one of the most critical issues that affect the environment, community health, economy, and management of urban areas. From a public health perspective, PM2.5 is one of the primary air pollutants, especially in Tehran's metropolis. Owing to the different patterns of PM2.5 in different seasons, Spatio-temporal modeling and identification of high-risk areas to reduce its effects seems necessary. The purpose of this study was Spatio-temporal modeling and preparation of PM2.5 risk mapping using three machine learning algorithms (random forest (RF), AdaBoost, and stochastic gradient descent (SGD)) in the metropolis of Tehran, Iran. Therefore, in the first step, to prepare the dependent variable data, the PM2.5 average was used for the four seasons of spring, summer, autumn, and winter. Then, using remote sensing (RS) and a geographic information system (GIS), independent data such as temperature, maximum temperature, minimum temperature, wind speed, rainfall, humidity, normalized difference vegetation index (NDVI), population density, street density, and distance to industrial centers were prepared as a seasonal average. To Spatio-temporal modeling using machine learning algorithms, 70% of the data were used for training and 30% for validation. The frequency ratio (FR) model was used as input to machine learning algorithms to calculate the spatial relationship between PM2.5 and the effective parameters. Finally, Spatio-temporal modeling and PM2.5 risk mapping were performed using three machine learning algorithms. The receiver operating characteristic (ROC) area under the curve (AUC) results showed that the RF algorithm had the greatest modeling accuracy, with values of 0.926, 0.94, 0.949, and 0.949 for spring, summer, autumn, and winter, respectively. According to the RF model, the most important variable in spring and autumn was NDVI. Temperature and distance to industrial centers were the most important variables in the summer and winter, respectively. The results showed that autumn, winter, summer, and spring had the highest risk of PM2.5, respectively.

Modeling trend changes of mean annual exposure to PM2.5 particles in the Middle East countries via growth mixture models

Rapid economic expansion, industrialization, urbanization, and construction in the Middle East have led to an increase in ambient air pollution. This study was devoted to model the 28-year trend changes of the mean annual exposure to particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5 particles) in the Middle East countries. Data used in the current study includes the mean annual exposure of PM2.5 air pollution (micrograms per cubic meter) for the Middle East countries during 1990 to 2017 that is compiled by the World Bank. To identify different longitudinal patterns of PM2.5, the researchers fitted growth mixture model with a different number of clusters in Mplus 7.4. The overall trend of annual exposure was rising during 1990 to 2017. Both the Bayesian information criterion and likelihood ratio test confirmed that the 2-cluster model is the best and the entropy of 0.99 confirmed a good quality in the cluster memberships. The observed pattern of exposure to PM2.5 in cluster 1, including Bahrain, Egypt, Iraq, Kuwait, Qatar, Saudi Arabia, and Yemen, was rising over time. On the other hand, countries in cluster 2, including Cyprus, Iran, Israel, Jordan, Oman, Lebanon, Syria, and Turkey, experienced a constant level of exposure to PM2.5 from 1990 to 2017. The main longitudinal patterns of PM2.5 were identified by the proposed model. Given the harmful effects of exposure to PM2.5 particles, taking into account more investigation in the future is highly suggested.

Fine Particle Exposure and Clinical Aggravation in Neurodegenerative Diseases in New York State.

Background:Adult-onset neurodegenerative diseases affect millions and negatively impact health care systems worldwide. Evidence suggests that air pollution may contribute to aggravation of neurodegeneration, but studies have been limited.Objective:We examined the potential association between long-term exposure to particulate matter in aerodynamic diameter [fine particulate matter ()] and disease aggravation in Alzheimer’s (AD) and Parkinson’s (PD) diseases and amyotrophic lateral sclerosis (ALS), using first hospitalization as a surrogate of clinical aggravation.Methods:We used data from the New York Department of Health Statewide Planning and Research Cooperative System (SPARCS 2000–2014) to construct annual county counts of first hospitalizations with a diagnosis of AD, PD, or ALS (total, urbanicity-, sex-, and age-stratified). We used annual concentrations estimated by a prediction model at a resolution, which we aggregated to population-weighted county averages to assign exposure to cases based on county of residence. We used outcome-specific mixed quasi-Poisson models with county-specific random intercepts to estimate rate ratios (RRs) for a 1-y exposure. We allowed for nonlinear exposure–outcome relationships using penalized splines and accounted for potential confounders.Results:We found a positive nonlinear association that plateaued above (, 95% CI: 1.04, 1.14 for a increase from 8.1 to ). We also found a linear positive association (, 95% CI: 1.01, 1.09 per increase), and suggestive evidence of an association with AD. We found effect modification by age for PD and ALS with a stronger positive association in patients of age but found insufficient evidence of effect modification by sex or urbanization level for any of the outcomes.Conclusion:Our findings suggest that annual increase in county-level concentrations may contribute to clinical aggravation of PD and ALS. Importantly, the average annual concentration in our study was , below the current American national standards, suggesting the standards may not adequately protect the aging population. https://doi.org/10.1289/EHP7425

Estimating hourly PM2.5 concentrations at the neighborhood scale using a low-cost air sensor network: A Los Angeles case study

Predicting PM2.5 concentrations at a fine spatial and temporal resolution (i.e., neighborhood, hourly) is challenging. Recent growth in low cost sensor networks is providing increased spatial coverage of air quality data that can be used to supplement data provided by monitors of regulatory agencies. We developed an hourly, 500 × 500 m gridded PM2.5 model that integrates PurpleAir low-cost air sensor network data for Los Angeles County. We developed a quality control scheme for PurpleAir data. We included spatially and temporally varying predictors in a random forest model with random oversampling of high concentrations to predict PM2.5. The model achieved high prediction accuracy (10-fold cross-validation (CV) R2 = 0.93, root mean squared error (RMSE) = 3.23 μg/m3; spatial CV R2 = 0.88, spatial RMSE = 4.33 μg/m3; temporal CV R2 = 0.90, temporal RMSE = 3.85 μg/m3). Our model was able to predict spatial and diurnal patterns in PM2.5 on typical weekdays and weekends, as well as non-typical days, such as holidays and wildfire days. The model allows for far more precise estimates of PM2.5 than existing methods based on few sensors. Taking advantage of low-cost PM2.5 sensors, our hourly random forest model predictions can be combined with time-activity diaries in future studies, enabling geographically and temporally fine exposure estimation for specific population groups in studies of acute air pollution health effects and studies of environmental justice issues.

Spatiotemporal patterns of PM2.5 elemental composition over China and associated health risks

Trace metals in atmospheric particulate matter (PM) are a serious threat to public health. Although pollution from toxic metals has been investigated in many Chinese cities, the spatial and temporal patterns in PM2.5 remain largely unknown. Long-term PM2.5 field sampling in 11 cities, combined with a systemic literature survey covering 51 cities, provides the first comprehensive database of 21 PM2.5-bound trace metals in China. Our results revealed that PM2.5 elemental compositions varied greatly, with generally higher levels in North China, especially for crustal elements. Pollution with Cr, As, and Cd was most serious, with 61, 38, and 16 sites, respectively, surpassing national standards, including some in rural areas. Local emissions, particularly from metallurgical industries, were the dominant factors driving the distribution in polluted cities such as Hengyang, Yuncheng, and Baiyin, which are mainly in North and Central China. Elevated As, Cd, and Cr levels in Yunnan, Guizhou Province within Southwest China were attributed to the high metal content of local coal. Diverse temporal trends of various elements that differed among regions indicated the complexity of emission patterns across the country. The results demonstrated high non-carcinogenic risks for those exposed to trace metals, especially for children and residents of heavily cities highly polluted with As, Pb, or Mn. The estimated carcinogenic risks ranged from 6.61 × 10−6 to 1.92 × 10−4 throughout China, with As being the highest priority element for control, followed by Cr and Cd. Regional diversity in major toxic metals was also revealed, highlighting the need for regional mitigation policies to protect vulnerable populations.

Influence of zero-burning policy and climate phenomena on ambient PM2.5 patterns and PAHs inhalation cancer risk during episodes of smoke haze in Northern Thailand

Abstract Air pollution from open burning in the Southeast Asian region has become an important environmental issue in the past decade, mainly due to its impacts on the economy and human health. Daily samples (24-h sampling) of particulate matter having an aerodynamic diameter equal to or less than 2.5 μm (PM2.5) were collected during the dry season (March–April) in the years 2017 and 2018 from two sampling sites: one in a suburban area of Chiang Mai Province (CM) that is regularly impacted by forest fires during the dry season, and one near an agricultural residue burning source located in Nan Province (NN). Daily average PM2.5 concentrations measured in Chiang Mai from both years (around 38–42 μg/m3) were about 2 times lower when compared to the same time period prior to the implementation of the zero-burning policy (2012–2015) with an average PM2.5 of 67.1 ± 34.1 μg/m3. Comparisons made during La Nina years (which are usually less affected by open burning), i.e. in 2012 (56 μg/m3), make it clear that a drop in PM2.5 values was influenced by the implementation of the zero-burning policy that was enforced for a period of about 60 days in an attempt to control open burning practices in upper Northern Thailand. Based on the patterns of PM2.5 concentrations observed during the dry season, it was determined that the policy could reduce open burning activities during the periods of the policy implementation. However, the zero-burning policy has also contributed to prolonging the smoke haze situation from a 2-month period (mid Feb –mid Apr) to a 3-month-long period (mid Feb–mid May). Moreover, health risks from the inhalation of PM2.5 bound PAHs is of great concern. PM2.5 concentrations at the two study sites were not significantly different, but average concentrations of 16 PAHs at the NN station for both years ( 5 ng/m3) were higher than those at the CM station ( 2 ng/m3). Therefore, exposure to PM2.5 at Nan area was more hazardous than at Chiang Mai. The ratio of some PAHs, i.e. BaA/(BaA + CHR) and IND/(IND + BPER), were used to determine the potential pollutant sources during the dry season, and it was found that PM2.5 at both sampling sites probably originated from similar source origins, i.e. wide-spread biomass burning (major source) and traffic emissions (minor source). In addition, not only are fine particulate matters a concern, but their chemical composition, particularly the levels of carcinogenic compounds that are bound to the PMs, are also believed to be a significant cause of these adverse health effects.

Sources of pollution and interrelationships between aerosol and precipitation chemistry at a central California site

This study examines co-located aerosol and precipitation chemistry data between 2010 and 2016 at Pinnacles National Monument ~65 km east of the coastline in central California. Positive matrix factorization analysis of the aerosol composition data revealed seven distinct pollutant sources: aged sea salt (25.7% of PM2.5), biomass burning (24.2% of PM2.5), fresh sea salt (15.0% of PM2.5), secondary sulfate (11.7% of PM2.5), dust (10.0% of PM2.5), vehicle emissions (8.2% of PM2.5), and secondary nitrate (5.2% of PM2.5). The influence of meteorology and transport on monthly patterns of PM2.5 composition is discussed. Only secondary sulfate exhibited a statistically significant change (a reduction) over time among the PM2.5 source factors. In contrast, PMcoarse exhibited a significant increase most likely due to dust influence. Monthly profiles of precipitation chemistry are summarized showing that the most abundant species in each month was either SO42−, NO3−, or Cl−. Intercomparisons between the precipitation and aerosol data revealed several features: (i) precipitation pH was inversely related to factors associated with more acidic aerosol constituents such as secondary sulfate and aged sea salt, in addition to being reduced by uptake of HNO3 in the liquid phase; (ii) two aerosol source factors (dust and aged sea salt) and PMcoarse exhibited a positive association with Ca2+ in precipitation, suggestive of directly emitted aerosol types with larger sizes promoting precipitation; and (iii) sulfate levels in both the aerosol and precipitation samples analyzed were significantly correlated with dust and aged sea salt PMF factors, pointing to the partitioning of secondary sulfate to dust and sea salt particles. The results of this work have implications for the region's air quality and hydrological cycle, in addition to demonstrating that the use of co-located aerosol and precipitation chemistry data can provide insights relevant to aerosol-precipitation interactions.

Impact of Obesity and Ozone on the Association Between Particulate Air Pollution and Cardiovascular Disease and Stroke Mortality Among US Adults.

BackgroundCardiovascular diseases (CVDs) and stroke are the highest and third highest causes of death, respectively, in the whole United States. It is well established that both long‐ and short‐term exposure to particulate air pollution (particulate matter with diameters <2.5 μm [PM 2.5]) increases the risks of both CVD and stroke mortality.Methods and ResultsWe combined county‐level data for CVD and stroke mortality, and prevalence of hypertension and obesity, with spatial patterns of PM 2.5 and ozone in a cross‐sectional ecological study. We found significant positive associations between both CVD (β=15.4, P<0.001) and stroke (β=2.7, P<0.001) mortality with PM 2.5. Ozone had significant link with just CVD (β=1372.1, P<0.001). Once poverty, ethnicity, and education were taken into account, there were still significant positive associations between PM 2.5 and both CVD (β=1.2, P<0.001) and stroke (β=1.1, P<0.001) mortality. Moreover, the association between CVD and ozone remained after adjustment for these factors (β=21.8, P<0.001). PM 2.5 and ozone were independent risk factors. The impact of PM 2.5 on CVD and stroke mortality was strongly dependent on the prevalence of obesity. Hypertension partially mediated the associations of PM 2.5 and mortality from CVD and stroke.ConclusionsThere was a spatial association between PM 2.5 exposure and the leading causes of death and disability in United States. The effect of PM 2.5 was considerably greater in areas where obesity is more prevalent. Hypertension is a possible mediator of the association of PM 2.5 and both CVD and stroke.

Socioeconomic and urban-rural differentials in exposure to air pollution and mortality burden in England

BackgroundSocioeconomically disadvantaged populations often have higher exposures to particulate air pollution, which can be expected to contribute to differentials in life expectancy. We examined socioeconomic differentials in exposure and air pollution-related mortality relating to larger scale (5 km resolution) variations in background concentrations of selected pollutants across England.MethodsOzone and particulate matter (sub-divided into PM10, PM2.5, PM2.5–10, primary, nitrate and sulphate PM2.5) were simulated at 5 km horizontal resolution using an atmospheric chemistry transport model (EMEP4UK). Annual mean concentrations of these pollutants were assigned to all 1,202,578 residential postcodes in England, which were classified by urban-rural status and socioeconomic deprivation based on the income and employment domains of the 2010 English Index of Multiple Deprivation for the Lower-level Super Output Area of residence. We used life table methods to estimate PM2.5-attributable life years (LYs) lost in both relative and absolute terms.ResultsConcentrations of the most particulate fractions, but not of nitrate PM2.5 or ozone, were modestly higher in areas of greater socioeconomic deprivation. Relationships between pollution level and socioeconomic deprivation were non-linear and varied by urban-rural status. The pattern of PM2.5 concentrations made only a small contribution to the steep socioeconomic gradient in LYs lost due to PM2.5 per 103 population, which primarily was driven by the steep socioeconomic gradient in underlying mortality rates. In rural areas, the absolute burden of air pollution-related LYs lost was lowest in the most deprived deciles.ConclusionsAir pollution shows modest socioeconomic patterning at 5 km resolution in England, but absolute attributable mortality burdens are strongly related to area-level deprivation because of underlying mortality rates. Measures that cause a general reduction in background concentrations of air pollution may modestly help narrow socioeconomic differences in health.

Modeling Urban PM2.5 Concentration by Combining Regression Models and Spectral Unmixing Analysis in a Region of East China

Understanding the spatial distribution of PM2.5 concentration and its contributing environmental variables is critical to develop strategies of addressing adverse effects of the particulate pollution. In this study, a range of meteorological and land use factors were incorporated into a linear regression (LR) model and a logistic model-based regression (LMR) model to simulate the annual and winter PM2.5 concentrations. The vegetation cover, derived from a linear spectral unmixing analysis (LSUA), and the normalized difference built-up index (NDBI), were found to improve the goodness of fit of the models. The study shows that (1) both the LR and the LMR agree on the predicted spatial patterns of PM2.5 concentration and (2) the goodness of fit is higher for the models established based on the annual PM2.5 concentration than that based on the winter PM2.5. The modeling results show that higher PM2.5 concentration coincided with the major urban area for the annual average but focused on the suburban and rural areas for the winter. The methods introduced in this study can potentially be applied to similar regions in other developing countries.

Effects of Ambient Fine Particles PM2.5 on Human HaCaT Cells.

The current study was conducted to observe the effects of fine particulate matter (PM2.5) on human keratinocyte cell line (HaCaT) cells. The potential mechanism linking PM2.5 and skin was explored. HaCaT cells were cultured and then accessed in plate with PM2.5. Cell viability was tested by Cell Counting Kit-8. The mRNA and protein expression of Filaggrin, Loricrin, Involucrin, and Repetin were analyzed. The levels of Granulocyte-macrophage Colony Stimulating Factor, Thymic Stromal Lymphopoietin, Tumor Necrosis Factor-α, Interleukin-1α, and Interleukin-8 were detected in the supernatant of the HaCaT cell with enzyme-linked immunosorbent assay kits. Cell viability decreased with the increase in PM2.5. Compared with the control group, the protein expression of Filaggrin, Repetin, Involucrin, and Loricrin showed different expression patterns in PM2.5 treatment groups. The level of Tumor Necrosis Factor-α, Thymic Stromal Lymphopoietin, Interleukin-1α, and Interleukin-8 significantly increased in the cells treated with PM2.5. Ambient PM2.5 may increase the risk of eczema and other skin diseases. The relative mechanism may be associated with the impairment of the skin barrier and the elevation of inflammatory responses.

Temporal Patterns in Fine Particulate Matter Time Series in Beijing: A Calendar View.

Extremely high fine particulate matter (PM2.5) concentration has become synonymous to Beijing, the capital of China, posing critical challenges to its sustainable development and leading to major public health concerns. In order to formulate mitigation measures and policies, knowledge on PM2.5 variation patterns should be obtained. While previous studies are limited either because of availability of data, or because of problematic a priori assumptions that PM2.5 concentration follows subjective seasonal, monthly, or weekly patterns, our study aims to reveal the data on a daily basis through visualization rather than imposing subjective periodic patterns upon the data. To achieve this, we conduct two time-series cluster analyses on full-year PM2.5 data in Beijing in 2014, and provide an innovative calendar visualization of PM2.5 measurements throughout the year. Insights from the analysis on temporal variation of PM2.5 concentration show that there are three diurnal patterns and no weekly patterns; seasonal patterns exist but they do not follow a strict temporal division. These findings advance current understanding on temporal patterns in PM2.5 data and offer a different perspective which can help with policy formulation on PM2.5 mitigation.

Chemical composition of PM2.5 and meteorological impact among three years in urban Shanghai, China

Air pollution is a major environmental concern in Yangtze River Delta (YRD) region in East China. Here we presented 3-year on-line measurements of PM2.5 chemical components from January 2011 to December 2013 at an urban site in Shanghai of YRD region. The 3-year average of PM2.5 mass concentration was 47 μg/m3, 34% higher than the second grade of China National Ambient Air Quality Standards for PM2.5 annual concentration. Monthly patterns of PM2.5 mass concentration and chemical components were observed, which were mainly influenced by the meteorological conditions through long-range transport and ventilation, and by the secondary species transformation. Significant year-to-year differences of PM2.5 characterization were observed during 2011–2013, and it can be attributed to the differences of regional transport as well as the wet deposition by precipitation. Generally, air mass from the western YRD region caused the most polluted air in Shanghai, while, air masses from the East China Sea had the lowest particle pollution because of marine time background air, and those from the surrounding region of Bo Hai in the north of China caused the moderate PM2.5 pollution in Shanghai. A large decrease of PM2.5 mass concentration, ∼44%, was observed for the fifteen years. In terms of chemical components, persistent decrease was observed for element carbon with a decrease of 70% but not for organic carbon, and the significant decrease of secondary inorganic ions was observed before year of 2008 but the persistent increase started after that.

Short-term population-based non-linear concentration-response associations between fine particulate matter and respiratory diseases in Taipei (Taiwan): a spatiotemporal analysis.

Fine particulate matter <2.5 μm (PM2.5) has been associated with human health issues; however, findings regarding the influence of PM2.5 on respiratory disease remain inconsistent. The short-term, population-based association between the respiratory clinic visits of children and PM2.5 exposure levels were investigated by considering both the spatiotemporal distributions of ambient pollution and clinic visit data. We applied a spatiotemporal structured additive regression model to examine the concentration-response (C-R) association between children's respiratory clinic visits and PM2.5 concentrations. This analysis was separately performed on three respiratory disease categories that were selected from the Taiwanese National Health Insurance database, which includes 41 districts in the Taipei area of Taiwan from 2005 to 2007. The findings reveal a non-linear C-R pattern of PM2.5, particularly in acute respiratory infections. However, a PM2.5 increase at relatively lower levels can elevate the same-day respiratory health risks of both preschool children (<6 years old) and schoolchildren (6-14 years old). In preschool children, same-day health risks rise when concentrations increase from 0.76 to 7.44 μg/m(3), and in schoolchildren, same-day health risks rise when concentrations increase from 0.76 to 7.52 μg/m(3). Changes in PM2.5 levels generally exhibited no significant association with same-day respiratory risks, except in instances where PM2.5 levels are extremely high, and these occurrences do exhibit a significant positive influence on respiratory health that is especially notable in schoolchildren. A significant high relative rate of respiratory clinic visits are concentrated in highly populated areas. We highlight the non-linearity of the respiratory health effects of PM2.5 on children to investigate this population-based association. The C-R relationship in this study can provide a highly valuable alternative for assessing the effects of ambient air pollution on human health.

Temporal variations, acidity, and transport patterns of PM2.5 ionic components at a background site in the Yellow River Delta, China

To better understand the pollution characteristics and potential sources of PM2.5 ionic components at the Yellow River Delta (YRD), a semicontinuous measurement was conducted to observe water-soluble ions in PM2.5 at a nature reserve in Dongying of Shandong province, China, in 2011. The results showed that SO4 2−, NO3 −, and NH4 + were the dominant ionic species (constituting 93 % of the total ionic mass) with their annual average concentrations of 22.48, 12.77, and 11.21 μg/m3, respectively. These three ion concentrations were generally lower than those observed in major cities in China but higher than those in other rural and nature reserve sites. Ion concentrations exhibited large seasonal variations, and maximum values were observed in summer. SO4 2− concentration presented a daytime peak in summer, autumn, and winter, while in spring, a relative flat diurnal cycle was observed. NO3 − concentration changed with that of SO4 2− during most of measurement period. Transport from surrounding areas contributed to the diurnal cycle of secondary ions. In addition, photochemical reaction and thermodynamic equilibrium played important roles on the diurnal variation of SO4 2− and NO3 −, respectively. The aerosol at the YRD was weakly acidic, and it was most acidic in winter. A cluster analysis showed that fine particle pollution at the YRD was mainly affected by southwest local emissions and northern middle- to long-distance transport.

A framework to spatially cluster air pollution monitoring sites in US based on the PM2.5 composition

BackgroundHeterogeneity in the response to PM2.5 is hypothesized to be related to differences in particle composition across monitoring sites which reflect differences in source types as well as climatic and topographic conditions impacting different geographic locations. Identifying spatial patterns in particle composition is a multivariate problem that requires novel methodologies. ObjectivesUse cluster analysis methods to identify spatial patterns in PM2.5 composition. Verify that the resulting clusters are distinct and informative. Methods109 monitoring sites with 75% reported speciation data during the period 2003–2008 were selected. These sites were categorized based on their average PM2.5 composition over the study period using k-means cluster analysis. The obtained clusters were validated and characterized based on their physico-chemical characteristics, geographic locations, emissions profiles, population density and proximity to major emission sources. ResultsOverall 31 clusters were identified. These include 21 clusters with 2 or more sites which were further grouped into 4 main types using hierarchical clustering. The resulting groupings are chemically meaningful and represent broad differences in emissions. The remaining clusters, encompassing single sites, were characterized based on their particle composition and geographic location. ConclusionsThe framework presented here provides a novel tool which can be used to identify and further classify sites based on their PM2.5 composition. The solution presented is fairly robust and yielded groupings that were meaningful in the context of air-pollution research.