Correlation Coefficients Of PM2 Research Articles

Statistical PM2.5 Prediction in an Urban Area Using Vertical Meteorological Factors

A key concern related to particulate air pollution is the development of an early warning system that can predict local PM2.5 levels and excessive PM2.5 concentration episodes using vertical meteorological factors. Machine learning (ML) algorithms, particularly those with recognition tasks, show great potential for this purpose. The objective of this study was to compare the performance of multiple linear regression (MLR) and multilayer perceptron (MLP) in predicting PM2.5 levels. The software was trained to predict PM2.5 levels up to 7 days in advance using data from long-term measurements of vertical meteorological factors taken at five heights above ground level (AGL)—10, 30, 50, 75, and 110 m—and PM2.5 concentrations measured 30 m AGL. The data used were collected between 2015 and 2020 at the Microclimate and Air Pollutants Monitoring Tower station at Kasetsart University, Bangkok, Thailand. The results showed that the correlation coefficients of PM2.5 predicted and observed using MLR and MLP were in the range of 0.69–0.86 and 0.64–0.82, respectively, for 1–3 days ahead. Both models showed satisfactory agreement with the measured data, and MLR performed better than MLP at PM2.5 prediction. In conclusion, this study demonstrates that the proposed approach can be used as a component of an early warning system in cities, contributing to sustainable air quality management in urban areas.

Simulation of the Spatiotemporal Distribution of PM2.5 Concentration Based on GTWR-XGBoost Two-Stage Model: A Case Study of Chengdu Chongqing Economic Circle

Natural environmental factors and human activity intensity factors, the two main factors that affect the spatial and temporal distribution of PM2.5 concentration near the surface, have different mechanisms of action on PM2.5 concentration. In this paper, a GTWR-XGBoost two-stage sequential hybrid model is proposed aiming at detecting the expression of spatiotemporal heterogeneity in the traditional machine learning retrieval model of PM2.5 concentration and the difficulty of expressing the complex nonlinear relationship in the statistical regression model. In the first stage, the natural environmental factors are used to predict PM2.5 concentration with spatiotemporal characteristics by collinearity diagnosis method and Geographically and Temporally Weighted Regression method (GTWR). In the second stage, the simulation results in the first stage and the natural factors eliminated through LUR stepwise regression in the first stage are into the XGBoost model together with the human activity intensity factors in the buffer zone with the best correlation coefficient of PM2.5, and finally the temporal and spatial distribution of PM2.5 concentration. Taking the Chengdu Chongqing Economic Circle as an example, the proposed model is used to retrieve PM2.5 concentration and compared with the single GTWR, XGBoost, and coupling model published recently. The experimental results show that the R2, RMSE, and MAE of the GTWR-XGBoost two-stage model cross-validation are 0.92, 5.44 ug·m−3, and 4.12 ug·m−3, respectively. Compared with the above single models, R2 increased by 0.01 and 0.12, and MAE decreased by more than 0.11 and 3.1, respectively. Compared with the coupling model published recently, R2 is increased by 0.02, and MAE is reduced by more than 0.4. In addition, the PM2.5 concentration in Chengdu Chongqing showed obvious seasonal temporal and spatial changes, and the influence ratios of natural environmental factors and human activity intensity activities factors on PM2.5 were 0.66 and 0.34. The results show that the GTWR-XGBoost two-stage Model can not only describe the heterogeneity and objectively reflect the complex nonlinear relationship between the phenomenon and the influencing factors, but also enhance the interpretability of the phenomenon when simulating the spatiotemporal distribution characteristics of PM2.5 concentration.

Spatiotemporal heterogeneity analysis of air quality in the Yangtze River Delta, China

Air pollution is a great concern due to its adverse health effects, and reducing air pollution is a major challenge worldwide. Utilizing the air quality monitoring data of the Yangtze River Delta (YRD) from 2016 to 2018, this paper first investigates the spatiotemporal patterns of the Air Quality Index (AQI), identifies the primary pollutants for each metropolitan area using gray relational analysis (GRA) method, and analyzes the spatial heterogeneity of the effects of six influencing factors on AQI in different metropolitan areas using geographically weighted regression (GWR). The study results show that: (1) The annual averages of the AQI in the YRD region from 2016 to 2018 are 78.6, 74.2, and 69.7, respectively, exhibiting a pattern of year-to-year decrease. (2) Among the 41 metropolitan areas of the YRD, the correlation coefficients of PM2.5 across metropolitan areas are between 0.75 and 0.95. PM2.5 is the primary pollutant in 36 metropolitan areas, accounting for 88% of the pollution in the studied areas. (3) The study results demonstrate that the six impact factors have positive or negative correlations with AQI with significant spatial differences. The mean values of the regression coefficients indicate that relative humidity has the greatest effect on AQI values, followed by precipitation, temperature, wind speed, air pressure, and Normalized Difference Vegetation Index (NDVI); of which relative humidity has a negative correlation with AQI while air pressure has positive correction with AQI in the whole study area. Since there is spatial heterogeneity on the degree of influence of each influencing factor on AQI, it is necessary to consider the spatial variability on the degree of influence different from indicators when formulating control countermeasures of air quality, so that the management countermeasures can be more targeted.

Improved Modeling of Spatiotemporal Variations of Fine Particulate Matter Using a Three‐Dimensional Variational Data Fusion Method

AbstractThe spatiotemporal concentration of multiple pollutants is crucial information for pollution control strategies to safeguard public health. Despite considerable efforts, however, significant uncertainty remains. In this study, a three‐dimensional variational model is coupled with a data assimilation (DA) system to analyze the spatiotemporal variation of PM2.5 for the whole of China. Monthly simulations of six sensitivity scenarios in different seasons, including different assimilation cycles, are carried out to assess the impact of the assimilation frequency on the PM2.5 simulations and the model simulation accuracy afforded by DA. The results show that the coupled system provides more reliable initial fields to substantially improve the model performance for PM2.5, PM10, and O3. Higher assimilation frequency improves the simulation in all geographic areas. Two statistical indicators—the root mean square error and the correlation coefficient of PM2.5 mass concentrations in the analysis field—are improved by 12.19 µg/m3 (33%) and 0.21 (48%), respectively. Although the 24‐h assimilation cycle considerably improves the model, assimilation at a 6‐h cycle raises the performance for PM2.5 to the performance goal level. The analysis shows that assimilating at a 24‐h cycle diminishes over time, whereas the positive impact of the 6‐h cycle persists. One pivotal finding is that the assimilation of PM2.5 in the outermost domain results in a substantial improvement in PM2.5 prediction for the innermost domain, which is a potential alternative method to the existing domain‐wide data fusion algorithm. The effect of assimilation varies among topographies, a finding that provides essential support for further model development.

Quantification of different processes in the rapid formation of a regional haze episode in north China using an integrated analysis tool coupling source apportionment with process analysis

North China Plain (NCP) is one of the most of heavily polluted regions in the world, air pollution associated with haze threatens human health. A high PM 2.5 concentration event in the NCP from 30 November to 10 December 2017 was simulated and analyzed by using the Weather Research and Forecasting (WRF) model and the Nested Air Quality Prediction Modeling System (NAQPMS) with an integrated analysis tool coupling source apportionment with process analysis. The weather field simulated by the WRF model and the PM 2.5 concentration simulated by NAQPMS agreed well with observations, and the correlation coefficient of PM 2.5 between the simulation and observation data remained >0.8 during the study period. We found that this high PM 2.5 event can be divided into three phases in NCP: The accumulation of PM 2.5 in Phase I was slow and dominated by south or weak winds in the stable boundary layer. In Phase II, PM 2.5 concentrations kept a high value in the short term under northern winds resulting from the edge of a cold front. The sustainable high value of PM 2.5 concentration in the middle and south of the NCP under northly wind has been less reported. An integrated process contribution analysis and source apportionment technology coupled with NAQPMS showed that the PM 2.5 in the north and middle of the NCP were transported to the southern area by the horizontal transmission process. This transport kept PM 2.5 high in the south of the NCP and even exceeded the contribution of local emissions. In the Phase III, the strong northern winds from main body of the cold front brought clear air masses and caused the PM 2.5 decrease in the whole NCP. The study shows that high dense emissions in the middle of NCP are the main cause for the high PM 2.5 events in the whole region, and the north wind could transport the pollutants form upstream region to downstream region, causing the continued high PM 2.5 in the middle and south of NCP. • An integrated process analysis and source apportionment technology was developed in chemical transport model. • The north winds caused accumulation of PM 2.5 by the horizontal transport in the north of North China Plain., • The main body of cold front greatly decreased PM 2.5 concentrations.

Temporal and Spatial Features of the Correlation between PM2.5 and O3 Concentrations in China.

In recent years, particulate matter of 2.5 µm or less (PM2.5) pollution in China has decreased but, at the same time, ozone (O3) pollution has become increasingly serious. Due to the different research areas and research periods, the existing analyses of the correlation between PM2.5 and O3 have reached different conclusions. In order to clarify the relationship between PM2.5 and O3, this study selected mainland China as the research area, based on the PM2.5 and O3 concentration data of 1458 air quality monitoring stations, and analyzed the correlation between PM2.5 and O3 for different time scales and geographic divisions. Moreover, by combining the characteristics of the pollutants, topography, and climatic features of the study area, we attempted to discuss the causes of the spatial and temporal differences of R-PO (the correlation between PM2.5 and O3). The study found that: (1) R-PO tends to show a positive correlation in summer and a negative correlation in winter, (2) the correlation coefficient of PM2.5 and O3 is lower in the morning and higher in the afternoon, and (3) R-PO also shows significant spatial differences, including north–south differences and coastland–inland differences.

Abstract 81: Spatial Pattern and Ecologic Analysis of the Impact of Air Pollution on the Prevalence of Stroke, Coronary Heart Disease and Diabetes Mellitus, and Premature Death in the United States

We aimed to examine the associations of air pollutants and climate change with prevalence of stroke, coronary heart disease (CHD), diabetes (DM) and premature mortality. We used 2010-2013 daily monitoring air pollution data across 200 to 600 counties of 50 U.S. states, and health outcomes data from U.S. EPA and CDC. Six common air pollutant indicators were studied: particulate matters (PM2.5 and PM10), carbon monoxide (CO), nitrogen dioxide (NO), Ozone (O3), sulfur dioxide (SO2), and lead. Monthly, seasonally, and then a 4-yearly average pollutant level was estimated for each county. Air pollution index was weighted by population density. The results show that counties with higher levels of air pollutant indexes had significantly higher prevalence of stroke, CHD, DM and premature death rate (defined as subjects died before 75 years old). After adjustment for county-level socioeconomic status (median household income), these associations remained significant, except for a non-significant association of lead with CHD (0.11, p=0.18) and DM (0.16, p=0.052). Stratification analysis by quartiles of county-level yearly average temperature (F) showed that associations between air pollutants and prevalence of stroke was much stronger (assessed by correlation coefficients) for residents who lived in areas where it had yearly average temperature of 44.3 F (Q1) as compared to those who lived in areas with annual average temperature of 51.2 F (Q2). The corresponding correlation coefficients of PM2.5, PM10, CO, NO, O3, SO2, and lead with stroke rates were 0.55, 0.61, 0.61, 0.64, 0.62, 0.63, 0.49, respectively in residents living in the areas with Q1 temperate, and were 0.43, 0.36, 0.34, 0.32, 0.42, 0.31, and 0.45, respectively in those living in the areas with Q2 temperature. A stronger association between air index and stroke was also observed for residents who lived in an average temperature of 56.7 F (Q3), and 67.7 F (Q4). Similar associations were observed between air pollution and CHD, DM and premature death. In conclusion, using nationally representative datasets the present study provides new evidence of a positive and significant association of air pollution with stroke, CHD, DM and premature death, and these associations are modified by air temperatures.

The Regression Models of PM2.5 and Other Air Pollutants in Wuhan

In this study PM2.5 and other five air pollutants (i.e. SO2, NO2, PM10, CO, and O3) have been analyzed by using statistical methods. Two different regression models were established quantitatively in this paper to find out the factors and causes of PM2.5 and were compared by using the partial data from January 1 to August 26, 2013, which is obtained from Wuhan Environmental Protection Bureau. The results indicate that the correlation coefficients of PM2.5 and other four air pollutants CO, SO2, NO2, and PM10 are all over 0.7, which shows significant linear correlations, and regression models were proved illustrious and accurate by testing and contrast.

The Indoor and Outdoor Carbonaceous Pollution during Winter and Summer in Rural Areas of Shaanxi, China

Indoor and outdoor concentrations of PM2.5, OC, EC and BC were investigated at three rural sites during the winter of 2007 and the summer of 2008 in Shaanxi, China. The average indoor concentrations of PM2.5, OC, EC and BC were found to be higher in winter (237.2 µg/m3, 61.1 µg/m3, 6.2 µg/m3, and 14.3 µg/m3, respectively) than in summer (99.0 µg/m3, 18.0 µg/m3, 2.8 µg/m3, and 7.4 µg/m3, respectively). The average indoor/outdoor (I/O) ratios for PM2.5 and EC were close to or less than 1.0 and those for OC and BC were above 1.0. The correlation coefficients of PM2.5 and carbon species also indicated that the indoor concentrations of PM2.5 and carbonaceous aerosol were correlated with the corresponding outdoor concentrations. Average OC/EC ratios ranged from 4.6 to 23.6 with an average of 9.1 during winter and from 4.2 to 12.0 with an average of 6.3 during summer. The results of this study showed that the OC and EC concentrations and OC/EC ratios in rural areas were higher than those of an urban area in Xi’an. These findings showed that improving energy efficiency and structure will be very important in reducing emissions in rural areas of Shaanxi.