Analysis of Factors Influencing Air Quality in Different Periods during COVID-19: A Case Study of Tangshan, China

Assessment of potential ecological risk for polycyclic aromatic hydrocarbons in urban soils with high level of atmospheric particulate matter concentration

Polycyclic aromatic hydrocarbons (PAHs) are known to be representative carcinogenic environmental pollutants with high toxicity. However, information on the potential ecological and environmental risks of PAH contamination in soil remains scarce. Thus, this study was evaluated the potential ecological risks of PAHs in soils of five Korean areas (Gunsan (GS), Gwangju, Yeongnam, Busan, and Gangwon) using organic carbon (OC)-normalized analysis, mean effect range-median quotient (M-ERM-Q), toxic equivalent quantity (TEQ) analysis, and risk quotient (RQ) derived by the species sensitivity distribution model. In this study, atmospheric particulate matter has a significant effect on soil pollution in GS through the presence of hopanes and the similar pattern of PAHs in soil and atmospheric PAHs. From analysis of source identification, combustion sources in soils of GS were important PAH sources. For PAHs in soils of GS, the OC-normalized analysis, M-ERM-Q, and TEQ analysis have 26.78 × 105 ng/g-OC, 0.218, and 49.72, respectively. Therefore, the potential ecological risk assessment results showed that GS had moderate-high ecological risk and moderate-high carcinogenic risk, whereas the other regions had low ecological risk and low-moderate carcinogenic risk. The risk level (M-ERM-Q) of PAH contamination in GS was similar to that in Changchun and Xiangxi Bay in China. The Port Harcourt City in Nigeria for PAH has the highest risk (M-ERM-Q = 4.02 and TEQ = 7923). Especially, compared to China (RQPhe =0.025 and 0.05), and Nigeria (0.059), phenanthrene showed the highest ecological risk in Korea (0.001-0.18). Korea should focus on controlling the release of PAHs originating from the PM in GS.

Spatiotemporal evolution characteristics and prediction analysis of urban air quality in China

To describe the spatiotemporal variations characteristics and future trends of urban air quality in China, this study evaluates the spatiotemporal evolution features and linkages between the air quality index (AQI) and six primary pollution indicators, using air quality monitoring data from 2014 to 2022. Seasonal autoregressive integrated moving average (SARIMA) and random forest (RF) models are created to forecast air quality. (1) The study's findings indicate that pollution levels and air quality index values in Chinese cities decline annually, following a "U"-shaped pattern with a monthly variation. The pollutant levels are high in winter and low in spring, and low in summer and rising in the fall (O₃ shows the opposite). (2) The spatial distribution of air quality in Chinese cities is low in the southeast and high in the northwest, and low in the coastal areas and higher in the inland areas. The correlation coefficients between AQI and the pollutant concentrations are as follows: fine particulate matter (PM_2.5), inhalable particulate matter (PM₁₀), carbon monoxide (CO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and ozone (O₃) values are correlated at 0.89, 0.84, 0.54, 0.54, 0.32, and 0.056, respectively. (3) In terms of short-term AQI predictions, the RF model performs better than the SARIMA model. The long-term forecast indicates that the average AQI value in Chinese cities is expected to decrease by 0.32 points in 2032 compared to the 2022 level of 52.95. This study has some guiding significance for the analysis and prediction of urban air quality.