Abstract

Ground-level ozone (O3) pollution has become a serious environmental issue in major urban agglomerations in China. To investigate the spatiotemporal patterns and regional transports of O3 in Beijing–Tianjin–Hebei (BTH-UA), the Yangtze River Delta (YRD-UA), the Triangle of Central China (TC-UA), Chengdu–Chongqing (CY-UA), and the Pearl River Delta urban agglomeration (PRD-UA), multiple transdisciplinary methods were employed to analyze the O3-concentration data that were collected from national air quality monitoring networks operated by the China National Environmental Monitoring Center (CNEMC). It was found that although ozone concentrations have decreased in recent years, ozone pollution is still a serious issue in China. O3 exhibited different spatiotemporal patterns in the five urban agglomerations. In terms of monthly variations, O3 had a unimodal structure in BTH-UA but a bimodal structure in the other urban agglomerations. The maximum O3 concentration was in autumn in PRD-UA, but in summer in the other urban agglomerations. In spatial distribution, the main distribution of O3 concentration was aligned in northeast–southwest direction for BTH-UA and CY-UA, but in northwest–southeast direction for YRD-UA, TC-UA, and PRD-UA. O3 concentrations exhibited positive spatial autocorrelations in BTH-UA, YRD-UA, and TC-UA, but negative spatial autocorrelations in CY-UA and PRD-UA. Variations in O3 concentration were more affected by weather fluctuations in coastal cities while the variations were more affected by seasonal changes in inland cities. O3 transport in the center cities of the five urban agglomerations was examined by backward trajectory and potential source analyses. Local areas mainly contributed to the O3 concentrations in the five cities, but regional transport also played a significant role. Our findings suggest joint efforts across cities and regions will be necessary to reduce O3 pollution in major urban agglomerations in China.

Highlights

  • Licensee MDPI, Basel, Switzerland.With its rapid industrialization and urbanization, China has experienced increased air pollution caused by fine particulate matter (PM2.5 ) and/or ozone (O3 ), which has drawn significant attention [1,2,3]

  • The potential source contribution function (PSCF), a widely used method to identify the probable locations of emission sources, is a conditional probability describing trajectories with pollutant concentrations larger than a given threshold passing through the receptor site [48]

  • To discuss the regional transport of O3 in five urban agglomerations, five regional center cities were selected for further study: Beijing in Beijing–Tianjin–Hebei urban agglomeration (BTH-UA), Shanghai in Yangtze River Delta (YRD)-UA, Wuhan in TC-UA, Chengdu in CY-UA, and Shenzhen in Pearl River Delta urban agglomeration (PRD-UA)

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Summary

Introduction

Many studies have studied the impact of regional background contributions to O3 concentration and identified the potential geographical source regions of O3 [20,21,22] These studies have suggested that the regional transport of ozone is significant to the overall issue. Based on the hourly O3 data observed from the ground real-time air quality monitoring network in China from 2017 to 2020, spatiotemporal patterns and regional transport of. We (1) systematically demonstrated the annual, seasonal, monthly, and diurnal variations in O3 concentrations, (2) analyzed the spatial patterns and variations in five urban agglomerations based on geographical analysis and spatial statistics, and (3) estimated the background contributions of O3 concentration and investigated the regional transport in different regions. The conclusions are conducive to determining the most efficient approach for O3 reduction in each urban agglomeration

Study Area
Data Source andground-level
Kernel-Density Estimation
Empirical Orthogonal Function Analysis
Standard Deviational Ellipse
Global and Local Spatial Autocorrelation Analysis
Texas Commission on Environmental Quality Method
Kolmogorov–Zurbenko Filter
Backward Trajectory
Potential Source Analysis
Annual Variation
Monthly variationofofMDA8
Diurnal
Standard Deviational Ellipse Analysis
O3 Time Series Separated by KZ Filter
Backward Trajectory Analysis
Potential Sources
Findings
Conclusions
Full Text
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