Research on the Temporal and Spatial Characteristics of Air Pollutants in Sichuan Basin

Abstract

Sichuan Basin is one of the most densely populated areas in China and the world. Human activities have great impact on the air quality. In order to understand the characteristics of overall air pollutants in Sichuan Basin in recent years, we analyzed the concentrations of six air pollutants monitored in 22 cities during the period from January 2015 to December 2020. During the study period, the annual average concentrations of CO, NO2, SO2, PM2.5 and PM10 all showed a clear downward trend, while the ozone concentration was slowly increasing. The spatial patterns of CO and SO2 were similar. High-concentration areas were mainly located in the western plateau of Sichuan Basin, while the concentrations of NO2 and particulate matter were more prominent in the urban agglomerations inside the basin. During the study period, changes of the monthly average concentrations for pollutants (except for O3) conformed to the U-shaped pattern, with the highest in winter and the lowest in summer. In the southern cities of the basin, secondary sources had a higher contribution to the generation of fine particulate matter, while in large cities inside the basin, such as Chengdu and Chongqing, air pollution had a strong correlation with automobile exhaust emissions. The heavy pollution incidents observed in the winter of 2017 were mainly caused by the surrounding plateau terrain with typical stagnant weather conditions. This finding was also supported by the backward trajectory analysis, which showed that the air masses arrived in Chengdu were mainly from the western plateau area of the basin. The results of this study will provide a basis for the government to take measures to improve the air quality in Sichuan Basin.

The Impact of Local Anti-Smog Resolution in Cracow (Poland) on the Concentrations of PM10 and BaP Based on the Results of Measurements of the State Environmental Monitoring

“…Many authors report the effect of air quality control efforts, including PM level changes in urban air [12][13][14] or going further, describe the consequent health benefits from particulate matter concentrations decreasing after policy implementation [15,16]. The problem of analyzing different policy tools is often raised [17].…”

Monitoring Surface 10Be/7Be Directly Reveals Stratospheric Air Intrusion in Sichuan Basin, China

“…In particular, an intense La Niña in late winter can strengthen the intensity of stratospheric intrusion in the following spring and significantly increase surface O 3 (Lin et al., 2015). In addition, the Sichuan Basin is one of the four major basins in China and one of the most densely populated areas of the world, with a total population of over 100 million (Fang et al., 2021). This has important human health effects for the study of changes in the atmospheric environment caused by the stratospheric air intrusion.…”

Study and Prediction of Air Quality in Smart Cities through Machine Learning Techniques Considering Spatiotemporal Components

“…A thorough study of these factors and phenomena should be carried out at the initial stages in order to identify all existing dependencies related to air quality, to find out which factors are more related, what to exclude and what to include in further analysis. These dependencies are connected both in the temporal and spatial dimensions 12 [16,17]. Dependencies in a temporal dimension refer to temporal relationships of a variable's value at time t and t − 1.…”

“…The plots were Figure 3.4: The time series of the concentration of nitrogen dioxide at stations with maximum values for each period in the city of Madrid (top: the station with id 72 during January-June 2019; bottom: the station with id 181 during January-June 2020). generated using plot acf () 16 and plot pacf () 17 functions from the statsmodels library 18 . The difference between autocorrelation and partial autocorrelation is that the former calculates the correlation between two lags while considering the influence of previous observations (direct and indirect effects), whereas the latter is simply a real correlation between two lags without intervening observations (only direct effects).…”

“…Another study confirming this belief was carried out by Zhao et al [7], which analysed the impact of air quality, in particular the impact of the actual reduction of industrial sulfur dioxide (SO 2 ) emissions on the SDGs in China. They determined that in China from 2005 to 2015 the actual reduction in industrial SO 2 emissions contributes 3.5% -12.3% of the actual change in the certain SDGs, including SDG 3,4,9,12,15,16,and 17. The above examples demonstrate the necessity of air quality monitoring, forecasting, and control. Among these three stages, forecasting air quality is the main target of the current work, which will allow decision-makers to control air quality within a range of acceptable thresholds, and as a result, prevent negative consequences caused by poor air quality.…”