Characteristics of air quality in different climatic zones of China during the COVID-19 lockdown

Impact of urban green spaces on air quality: A study of PM10 reduction across diverse climates

Urban areas face high particulate matter (PM10) levels, increasing the risk of respiratory and cardiovascular diseases. Green spaces can significantly reduce PM10 concentration, as shown at various scales, from boroughs to whole cities. However, long-term monitoring is needed to understand the specific mechanisms and cumulative impact of green spaces on air quality to changing pollution levels. We investigated the influence of neighbourhood green space percentage, climatic variables, and population density on PM10 deposition during the vegetation period across eight cities in contrasting climate zones over 20 years (2000-2020). We used a correlation matrix, generalized additive model, one-way ANOVA, and Tukey HSD test to analyze the impact of these factors on PM10 deposition rates, assess the role of green space percentage in reducing it, and identify significant differences in PM10 parameters at different proximities to emission sources. Cities with higher population density in warmer, drier climates had higher PM levels, since land surface temperature and wind pressure positively correlated with PM10 deposition, while relative humidity showed a negative correlation. The study found significantly higher PM10 concentrations in industrial areas (36.25 μg/m³) than in roadside areas (25.73 μg/m³) and parks (20.17 μg/m³) (p < 0.01). This highlights the need for targeted interventions in different zones. The study found a complex relationship between green space percentage and PM10 deposition rate onto plant surfaces. Our model suggests that at least 27% of green spaces as land cover can significantly reduce the particulate matter flux, although the minimum threshold can vary depending on the specific urban contexts. The study focused on the proportionate cover of green spaces; still, further investigation including quantitative aspects of urban surface forms, and traffic emissions can comprehend the climatic context and determine the optimal extent of green space required for strategic planning toward future urban sustainability initiatives.

A CNN-SVR model for NO2 profile prediction based on MAX-DOAS observations: The influence of Chinese New Year overlapping the 2020 COVID-19 lockdown on vertical distributions of tropospheric NO2 in Nanjing, China

In this study, a hybrid model, the convolutional neural network-support vector regression model, was adopted to achieve prediction of the NO2 profile in Nanjing from January 2019 to March 2021. Given the sudden decline in NO2 in February 2020, the contribution of the Coronavirus Disease-19 (COVID-19) lockdown, Chinese New Year (CNY), and meteorological conditions to the reduction of NO2 was evaluated. NO2 vertical column densities (VCDs) from January to March 2020 decreased by 59.05% and 32.81%, relative to the same period in 2019 and 2021, respectively. During the period of 2020 COVID-19, the average NO2 VCDs were 50.50% and 29.96% lower than those during the pre-lockdown and post-lockdown periods, respectively. The NO2 volume mixing ratios (VMRs) during the 2020 COVID-19 lockdown significantly decreased below 400 m. The NO2 VMRs under the different wind fields were significantly lower during the lockdown period than during the pre-lockdown period. This phenomenon could be attributed to the 2020 COVID-19 lockdown. The NO2 VMRs before and after the CNY were significantly lower in 2020 than in 2019 and 2021 in the same period, which further proves that the decrease in NO2 in February 2020 was attributed to the COVID-19 lockdown. Pollution source analysis of an NO2 pollution episode during the lockdown period showed that the polluted air mass in the Beijing-Tianjin-Hebei was transported southwards under the action of the north wind, and the subsequent unfavorable meteorological conditions (local wind speed of < 2.0 m/sec) resulted in the accumulation of pollutants.

Better understanding the behavior of air pollutants at shutdown times - results of a short full lockdown

Numerous studies have evaluated the effects of lockdowns during the COVID-19 pandemic, but most of them have concerned large cities and regions. This study aimed to evaluate the dynamics of air pollutants during and after the implementation of a short lockdown in the medium-sized city of Pelotas, Brazil, using hourly measurements of pollutants. The evaluation period included in this study was between August 9th and 12th, 2020. A machine learning model was used to investigate the expected behavior against what was observed during the study period. All pollutants presented a gradual reduction until a dynamic plateau established 48 hours after the start of the lockdown: NO2 (↓4%), O3 (↓34%), SO2 (↓24%), CO (↓48%), PM10 (↓82%) and PM2.5 (↓82%). At the end of the restriction measures, the PM10 and PM2.5 levels continued to decline beyond expectations. Our findings show that these measures can positively affect the air quality in medium-sized cities.

The effect of natural and socioeconomic factors on haze pollution from global and local perspectives in China

Analyzing the factors that cause haze and the regional differences in the influence of factors on haze is the premise and critical to precise prevention and control of haze pollution. This paper explores the global effects of haze pollution drivers and the spatial heterogeneity of factors on haze pollution using global and local regression models. The results show that, from a global perspective, a 1 μg/m³ increase in the average PM_2.5 concentration of a city's neighbors will increase the city's PM_2.5 concentration by 0.965 μg/m³. Temperature, atmospheric pressure, population density, and green coverage of built-up areas are positively associated with haze, while GDP per capita is the opposite. From a local perspective, each factor has different influencing scales on haze pollution. Specifically, technical support is on a global scale, and for every 1 unit increase in technical support level, the PM_2.5 concentration will decrease by 0.106-0.102 μg/m³. The influencing scales of other drivers are local. In southern China, the concentration of PM_2.5 decreases by 0.001-0.075 μg/m³ for every 1 °C increase in temperature, while in northern China, the concentration of PM_2.5 increases by 0.001-0.889 μg/m³. In the region around the Bohai Sea in eastern China, the concentration of PM_2.5 will decrease by 0.001-0.889 μg/m³ for every 1 m/s increase in wind speed. Population density positively impacts haze pollution, and the impact intensity gradually increases from 0.097 to 1.140 from south to north. For every 1% increase in the proportion of the secondary industry in southwest China, the PM_2.5 concentration will increase by 0.001-0.284 μg/m³. For cities in northeast China, for every 1% increase in the urbanization rate, the PM_2.5 concentration will decrease by 0.001-0.203 μg/m³. These findings help policymakers develop targeted joint prevention and control policies for haze pollution, considering regional differences.

Assessing the ambient air quality patterns associated to the COVID-19 outbreak in the Yangtze River Delta: A random forest approach

The novel coronavirus (COVID-19), first identified at the end of December 2019, has significant impacts on all aspects of human society. In this study, we aimed to assess the ambient air quality patterns associated to the COVID-19 outbreak in the Yangtze River Delta (YRD) region using a random forest (RF) model. To estimate the accuracy of the model, the cross-validation (CV), determination coefficient R2, root mean squared error (RMSE) and mean absolute error (MAE) were used. The results demonstrate that the RF model achieved the best performance in the prediction of PM10 (R2 = 0.78, RMSE = 8.81 μg/m3), PM2.5 (R2 = 0.76, RMSE = 6.16 μg/m3), SO2 (R2 = 0.76, RMSE = 0.70 μg/m3), NO2 (R2 = 0.75, RMSE = 4.25 μg/m3), CO (R2 = 0.81, RMSE = 0.4 μg/m3) and O3 (R2 = 0.79, RMSE = 6.24 μg/m3) concentrations in the YRD region. Compared with the prior two years (2018-19), significant reductions were recorded in air pollutants, such as SO2 (-36.37%), followed by PM10 (-33.95%), PM2.5 (-32.86%), NO2 (-32.65%) and CO (-20.48%), while an increase in O3 was observed (6.70%) during the COVID-19 period (first phase). Moreover, the YRD experienced rising trends in the concentrations of PM10, PM2.5, NO2 and CO, while SO2 and O3 levels decreased in 2021-22 (second phase). These findings provide credible outcomes and encourage the efforts to mitigate air pollution problems in the future.

Increased tropospheric ozone levels as a public health issue during COVID-19 lockdown and estimation the related pulmonary diseases

The aims of this study were to i) investigate the variation of tropospheric ozone (O3) levels during the COVID-19 lockdown; ii) determine the relationships between O3 concentrations with the number of COVID-19 cases; and iii) estimate the O3-related health effects in Southwestern Iran (Khorramabad) over the time period 2019-2021. The hourly O3 data were collected from ground monitoring stations, as well as retrieved from Sentinel-5 satellite data for showing the changes in O3 levels pre, during, and after lockdown period. The concentration-response function model was applied using relative risk (RR) values and baseline incidence (BI) to assess the O3-related health effects. Compared to 2019, the annual O3 mean concentrations increased by 12.2% in 2020 and declined by 3.9% in 2021. The spatiotemporal changes showed a significant O3 increase during COVID-19 lockdown, and a negative correlation between O3 levels and the number of COVID-19 cases was found (r = - 0.59, p < 0.05). In 2020, the number of hospital admissions for cardiovascular diseases increased by 4.0 per 105 cases, the mortality for respiratory diseases increased by 0.7 per 105 cases, and the long-term mortality for respiratory diseases increased by 0.9 per 105 cases. Policy decisions are now required to reduce the surface O3 concentrations and O3-related health effects in Iran.

The Long-Term Impact of COVID-19 Lockdowns in Istanbul

The World Health Organization (WHO) have set sustainability development goals to reduce diseases, deaths, and the environmental impact of cities due to air pollution. In Istanbul, although average pollutant concentrations have been on a downward trend in recent years, extreme values and their annual exceedance numbers are high based on the air quality standards of WHO and the EU. Due to COVID-19 lockdowns, statistically significant reductions in emissions were observed for short periods. However, how long the effect of the lockdowns will last is unknown. For this reason, this study aims to investigate the impact of long-term lockdowns on Istanbul's air quality. The restriction period is approximated to the same periods of the previous years to eliminate seasonal effects. A series of paired t-tests (p-value < 0.05) were applied to hourly data from 12 March 2016, until 1 July 2021, when quarantines were completed at 36 air quality monitoring stations in Istanbul. The findings reveal that the average air quality of Istanbul was approximately 17% improved during the long-term lockdowns. Therefore, the restriction-related changes in emission distributions continued in the long-term period of 476 days. However, it is unknown how long this effect will continue, which will be the subject of future studies. Moreover, it was observed that the emission probability density functions changed considerably during the lockdowns compared to the years before. Accordingly, notable decreases were detected in air quality limit exceedances in terms of both excessive pollutant concentrations and frequency of occurrence, respectively, for PM10 (-13% and -13%), PM2.5 (-16% and -30%), and NO2 (-3% and -8%), but not for O3 (+200% and +540%) and SO2 (-10% and +2.5%).

Factors Influencing O3 Concentration in Traffic and Urban Environments: A Case Study of Guangzhou City

Ozone (O3) pollution is a serious issue in China, posing a significant threat to people's health. Traffic emissions are the main pollutant source in urban areas. NOX and volatile organic compounds (VOCs) from traffic emissions are the main precursors of O3. Thus, it is crucial to investigate the relationship between traffic conditions and O3 pollution. This study focused on the potential relationship between O3 concentration and traffic conditions at a roadside and urban background in Guangzhou, one of the largest cities in China. The results demonstrated that no significant difference in the O3 concentration was observed between roadside and urban background environments. However, the O3 concentration was 2 to 3 times higher on sunny days (above 90 μg/m3) than on cloudy days due to meteorological conditions. The results confirmed that limiting traffic emissions may increase O3 concentrations in Guangzhou. Therefore, the focus should be on industrial, energy, and transportation emission mitigation and the influence of meteorological conditions to minimize O3 pollution. The results in this study provide some theoretical basis for mitigation emission policies in China.

Association of population migration with air quality: Role of city attributes in China during COVID-19 pandemic (2019-2021)

Atmospheric pollution studies have linked diminished human activity during the COVID-19 pandemic to improve air quality. This study was conducted during January to March (2019-2021) in 332 cities in China to examine the association between population migration and air quality, and examined the role of three city attributes (pollution level, city scale, and lockdown status) in this effect. This study assessed six air pollutants, namely CO, NO2, O3, PM10, PM2.5, and SO2, and measured meteorological data, with-in city migration (WCM) index, and inter-city migration (ICM) index. A linear mixed-effects model with an autoregressive distributed lag model was fitted to estimate the effect of the percent change in migration on air pollution, adjusting for potential confounding factors. In summary, lower migration was associated with decreased air pollution (other than O3). Pollution change in susceptibility is more likely to occur in NO2 decrease and O3 increase, but unsusceptibility is more likely to occur in CO and SO2, to city attributes from low migration. Cities that are less air polluted and population-dense may benefit more from decreasing PM10 and PM2.5. The associations between population migration and air pollution were stronger in cities with stringent traffic restrictions than in cities with no lockdowns. Based on city attributes, an insignificant difference was observed between the effects of ICM and WCM on air pollution. Findings from this study may gain knowledge about the potential interaction between migration and city attributes, which may help decision-makers adopt air-quality policies with city-specific targets and paths to pursue similar air quality improvements for public health but at a much lower economic cost than lockdowns.