Periods Of PM2 Research Articles

Understanding the Disparities of PM2.5 Air Pollution in Urban Areas via Deep Support Vector Regression.

In densely populated urban areas, PM2.5 has a direct impact on the health and quality of residents' life. Thus, understanding the disparities of PM2.5 is crucial for ensuring urban sustainability and public health. Traditional prediction models often overlook the spillover effects within urban areas and the complexity of the data, leading to inaccurate spatial predictions of PM2.5. We propose Deep Support Vector Regression (DSVR) that models the urban areas as a graph, with grid center points as the nodes and the connections between grids as the edges. Nature and human activity features of each grid are initialized as the representation of each node. Based on the graph, DSVR uses random diffusion-based deep learning to quantify the spillover effects of PM2.5. It leverages random walk to uncover more extensive spillover relationships between nodes, thereby capturing both the local and nonlocal spillover effects of PM2.5. And then it engages in predictive learning using the feature vectors that encapsulate spillover effects, enhancing the understanding of PM2.5 disparities and connections across different regions. By applying our proposed model in the northern region of New York for predictive performance analysis, we found that DSVR consistently outperforms other models. During periods of PM2.5 surges, the R-square of DSVR reaches as high as 0.729, outperforming non-spillover models by 2.5 to 5.7 times and traditional spatial metric models by 2.2 to 4.6 times. Therefore, our proposed model holds significant importance for understanding disparities of PM2.5 air pollution in urban areas, taking the first steps toward a new method that considers both the spillover effects and nonlinear feature of data for prediction.

Long-term exposure to PM2.5 and its components is associated with elevated blood pressure and hypertension prevalence: Evidence from rural adults

IntroductionThe toxicity of fine particulate matter (PM2.5) is determined by its components, while the evidence regarding associations of PM2.5 components with blood pressure (BP) is limited, especially in rural areas. ObjectivesThis study aimed to explore the associations of PM2.5 and its chemical components with systolic BP (SBP), diastolic BP (DBP), pulse pressure (PP), mean artery pressure (MAP) levels and hypertension prevalence, and to identify key components in Chinese rural areas. Methods39,211 adults from the Henan Rural Cohort were included during 2015–2017. Different periods of PM2.5 and chemical components were estimated by hybrid satellite model. The single-pollutant, component-PM2.5 model, component-residual model and component-proportion model were applied to explore the associations of pollutants with BP levels and hypertension prevalence. Exposure-response (E-R) relationships, stratified analyses and sensitivity analyses were used to explore these associations further. Results12,826 (32.71%) were identified with hypertension. For each 1 μg/m3 increase of pollutants, the adjusted odds ratio (OR) for hypertension prevalence was 1.03 for PM2.5 mass, 1.40 for BC, 1.16 for NH4+, 1.08 for NO3–, 1.17 for OM, 1.12 for SO42− and 1.25 for SOIL in the single-pollutant model. BC and SOIL were statistically significant in the component-PM2.5 model, component-residual model and component-proportion model. Similarly, associations of these pollutants with elevated BP levels were also found in aforementioned four models. These pollutants produced a stronger association with SBP than DBP, PP and MAP. Most of associations were non-linear in E-R relationships. The groups of older, the men, with lower per capita monthly income, lower educational level and higher BMI were more vulnerable to these pollutants in stratified analyses. The results remained stable in sensitivity analyses. ConclusionLong-term exposure to PM2.5 and its components, especially BC and SOIL, was associated with elevated BP and hypertension prevalence in rural adults, and decreasing pollutants may provide additional benefits.

The impacts of the meteorology features on PM2.5 levels during a severe haze episode in central-east China

The most polluted urban agglomeration including 13 cities in Central-East China (112–122°E, 34–42°N) were selected to study the impacts of meteorology features on PM2.5 levels during the severe haze episode by using observational PM2.5 concentration, surface and balloon sounding meteorology data. The study results showed that the temporal changing of PM2.5 in the 13 cities showed well correlation at the haze beginning, maintenance, and ending period due to the similar 500 hPa circulation and surface sea level pressure pattern. The increasing of surface relative humidity (RH) and temperature preceded PM2.5 accumulation when haze began. RH usually reached up to 90–95% during the period of PM2.5 peak, suggesting the possible contribution of high humidity to extreme PM2.5 values. In contrast to the similar circulation of upper air and surface pressure pattern, the divergences of local PBL meteorology, especially their vertical structure, were very obvious, which was the major meteorology cause for the different PM2.5 levels in these cities. The temperature rise at 850 hPa layer was higher than that at 925 hPa, which was higher than that at 1000 hPa, leading to the formation of temperature inversion. This was the most important trigger factor for haze. PM2.5 maximum generally occurred within 12 h after the formation of the strongest inversion in each city. PM2.5 levels in the 13 cities strongly depend on their reverse intensity: for the cities where the inversion was strong and long lasting, its PM2.5 was often the highest. Stable inversions are more likely to form in the transitional area from the northwestern mountains to the southeastern plains because of the mountain's blocking of cold air and the warming of boundary layer by sinking airflow from the mountaintop. This is the major meteorology cause for the frequent occurrence of the extreme PM2.5 levels in middle-south plain of Hebei province. This study reminds us that local boundary layer and inversion conditions, closely related with geographical location and local topography, contributes greatly to local PM2.5 levels and should be fully considered in the emission reduction and industrial layout policy by government.

Coupling between Chemical and Meteorological Processes under Persistent Cold-Air Pool Conditions: Evolution of Wintertime PM2.5 Pollution Events and N2O5 Observations in Utah's Salt Lake Valley.

The Salt Lake Valley experiences severe fine particulate matter pollution episodes in winter during persistent cold-air pools (PCAPs). We employ measurements throughout an entire winter from different elevations to examine the chemical and dynamical processes driving these episodes. Whereas primary pollutants such as NOx and CO were enhanced twofold during PCAPs, O3 concentrations were approximately threefold lower. Atmospheric composition varies strongly with altitude within a PCAP at night with lower NOx and higher oxidants (O3) and oxidized reactive nitrogen (N2O5) aloft. We present observations of N2O5 during PCAPs that provide evidence for its role in cold-pool nitrate formation. Our observations suggest that nighttime and early morning chemistry in the upper levels of a PCAP plays an important role in aerosol nitrate formation. Subsequent daytime mixing enhances surface PM2.5 by dispersing the aerosol throughout the PCAP. As pollutants accumulate and deplete oxidants, nitrate chemistry becomes less active during the later stages of the pollution episodes. This leads to distinct stages of PM2.5 pollution episodes, starting with a period of PM2.5 buildup and followed by a period with plateauing concentrations. We discuss the implications of these findings for mitigation strategies.

Using Atmospheric Visibility to Assess the Effects of Air Pollution on Hospital Admissions for Respiratory Diseases

Air pollution is a significant hazard to human health. Atmospheric visibility is an aggregative indicator of air pollution and meteorological conditions. Yet, only a few studies have reported the relationship between atmospheric visibility and health risks. This study attempted to utilize atmospheric visibility to assess the effects of air pollution on hospital admissions for respiratory diseases (ICD-9: 460-519). Daily measurements of air pollutants, atmospheric visibility, and Taiwan's National Health Insurance Research Database were obtained for the year 2010 in Taichung. We compared the differences in different locations (coastal and urban areas). A Poisson regression analysis was used to evaluate the relationship between the atmospheric visibility and the respiratory consultation rate, with adjustment for potential confounding factors, including meteorological factors, spatial variances, and types of air pollution. The primary results indicated that air pollution and atmospheric visibility degradation were both more serious in the urban area than in the coastal area. The atmospheric visibilities were divided into Q1 (25th percentile), Q2 (50th percentile), and Q3 (75th percentile); these divisions show the inversely proportional trend between atmospheric visibility range and health risks. It was also observed the similar inversely proportional trend between particulate matter and health risks. Under the hazard periods of atmospheric visibility ≤ 10 km, the relative significant health risks of 1.088 and 1.042 were observed in the coastal area and urban area, respectively. The relative health risks of particulate matters were 1.053 in the coastal area and 1.101 in the urban area during the periods of PM2.5 ≥ 35 µg m–3. This study establishes a new perspective from which to assess the effects of air pollution on hospital admissions for respiratory diseases by utilizing atmospheric visibility. Additionally, it provides a convenient way for the public to understand the relationship between air pollution and its effects on human health.