Spatiotemporal Characteristics of Air Quality across Weifang from 2014–2018

Li, Chengming; Dai, Zhaoxin; Yang, Lixin; Ma, Zhaoting

doi:10.3390/ijerph16173122

Cited by 17 publications

(11 citation statements)

References 34 publications

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“…Hence, we recommend that residents participate in outdoor sports as much as possible during this period to reduce the risk of respiratory or cardiovascular diseases caused by air pollution. Although this paper does not provide hourly characteristics of Dongying City in summer, according to a previous study of a similar city (Weifang), the best air quality usually occurs at 5 pm (Li et al, 2019). The finding of this paper supports Chen et al (2020), who recommended that residents in China go out in the afternoon.…”

Section: Accepted Manuscriptsupporting

confidence: 50%

COVID-19 as a Factor Influencing Air Quality? A City Study in China

Shang

Cui

et al. 2021

Aerosol Air Qual. Res.

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In late December 2019, coronavirus disease-2019 (COVID-19) emerged. To avert the spread of the pandemic, the Chinese government immediately adopted lockdown measures such as traffic restrictions and factory closure. By analyzing the spatio-temporal distribution of AQI simply in Dongying City, a city dominated by petroleum exploration, this study aims to investigate the impact of short-term lockdown in February 2020 on the air quality. The possible influence of meteorological factors and industry exhaust emissions on the air quality were also considered and assessed. Statistically significant air quality index (AQI) reduced were observed during the short-term lockdown period compared to the same period in 2019: a 24.6% reduction with absolute decrease of 25.4.The difference between the peak and trough value of hourly average AQI during the shortterm period was nearly 1/3 that of the value in winter. The COVID-19 pandemic also had slight effects on the spatial characteristics of the AQI. Quantitative analysis demonstrated that the AQI had a strong positive correlation with exhaust emissions with p<0.01, indicating that exhaust emissions contribute to air pollution. During the short-term period, the effect of exhaust emissions on the AQI in Dongying City was significantly reduced, with about 38.3% reduction. This study inferred that the decline in AQI is related to the less movement of traffic and enterprise closure. This research provides a valuable reference for controlling air pollution in Dongying City and other areas with similar conditions.

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Section: Accepted Manuscriptsupporting

confidence: 50%

COVID-19 as a Factor Influencing Air Quality? A City Study in China

Shang

Cui

et al. 2021

Aerosol Air Qual. Res.

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“…As a regional comprehensive transportation hub, Weifang city has been experiencing industrial and economic advancements; however, the air pollution accompanying these advancements is intensifying. According to the comprehensive air quality ranking of 168 key Chinese cities issued by the Ministry of Ecology and Environment in 2019, Weifang was ranked moderately low [ 18 , 19 ]. The terrain and land types in Weifang are complex and diverse, and the spatial distribution of these land types has important effects on air pollution.…”

Section: Methodsmentioning

confidence: 99%

Investigation of the Impact of Land-Use Distribution on PM2.5 in Weifang: Seasonal Variations

Zhang

Dai

et al. 2020

IJERPH

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As air pollution becomes highly focused in China, the accurate identification of its influencing factors is critical for achieving effective control and targeted environmental governance. Land-use distribution is one of the key factors affecting air quality, and research on the impact of land-use distribution on air pollution has drawn wide attention. However, considerable studies have mostly used linear regression models, which fail to capture the nonlinear effects of land-use distribution on PM2.5 (fine particulate matter with a diameter less than or equal to 2.5 microns) and to show how impacts on PM2.5 vary with land-use magnitudes. In addition, related studies have generally focused on annual analyses, ignoring the seasonal variability of the impact of land-use distribution on PM2.5, thus leading to possible estimation biases for PM2.5. This study was designed to address these issues and assess the impacts of land-use distribution on PM2.5 in Weifang, China. A machine learning statistical model, the boosted regression tree (BRT), was applied to measure nonlinear effects of land-use distribution on PM2.5, capture how land-use magnitude impacts PM2.5 across different seasons, and explore the policy implications for urban planning. The main conclusions are that the air quality will significantly improve with an increase in grassland and forest area, especially below 8% and 20%, respectively. When the distribution of construction land is greater than around 10%, the PM2.5 pollution can be seriously substantially increased with the increment of their areas. The impact of gardens and farmland presents seasonal characteristics. It is noted that as the weather becomes colder, the inhibitory effect of vegetation distribution on the PM2.5 concentration gradually decreases, while the positive impacts of artificial surface distributions, such as construction land and roads, are aggravated because leaves drop off in autumn (September–November) and winter (December–February). According to the findings of this study, it is recommended that Weifang should strengthen pollution control in winter, for instance, expand the coverage areas of evergreen vegetation like Pinus bungeana Zucc. and Euonymus japonicus Thunb, and increase the width and numbers of branches connecting different main roads. The findings also provide quantitative and optimal land-use planning and strategies to minimize PM2.5 pollution, referring to the status of regional urbanization and greening construction.

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“…The concentrations of six criteria pollutants, especially PM 2.5 , NO 2 , and O 3 , are frequently above the Chinese Ambient Air Quality Standard (CAAQS) [ 9 ]. The pollutants have caused the strong concern of the public, because long-term exposure to air pollution potentially poses adverse effects on human health, triggers cardiovascular and respiratory diseases, and causes damage to human immune systems [ 10 , 11 , 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Air Pollution Characteristics and Health Risks in the Yangtze River Economic Belt, China during Winter

Mao

Sun

Zhang

2020

IJERPH

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The air pollution characteristics of six ambient criteria pollutants, including particulate matter (PM) and trace gases, in 29 typical cities across the Yangtze River Economic Belt (YREB) from December 2017 to February 2018 are analyzed. The overall average mass concentrations of PM2.5, PM10, SO2, CO, NO2, and O3 are 73, 104, 16, 1100, 47, and 62 µg/m3, respectively. PM2.5, PM10, and NO2 are the dominant major pollutants to poor air quality, with nearly 83%, 86%, and 59%, exceeding the Chinese Ambient Air Quality Standard Grade I. The situation of PM pollution in the middle and lower reaches is more serious than that in the upper reaches, and the north bank is more severe than the south bank of the Yangtze River. Strong positive spatial correlations for PM concentrations between city pairs within 300 km is frequently observed. NO2 pollution is primarily concentrated in the Suzhou-Wuxi-Changzhou urban agglomeration and surrounding areas. The health risks are assessed by the comparison of the classification of air pollution levels with three approaches: air quality index (AQI), aggregate AQI (AAQI), and health risk-based AQI (HAQI). When the AQI values escalate, the air pollution classifications based on the AAQI and HAQI values become more serious. The HAQI approach can better report the comprehensive health effects from multipollutant air pollution. The population-weighted HAQI data in the winter exhibit that 50%, 70%, and 80% of the population in the upstream, midstream, and downstream of the YREB are exposed to polluted air (HAQI > 100). The current air pollution status in YREB needs more effective efforts to improve the air quality.

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Spatiotemporal Characteristics of Air Quality across Weifang from 2014–2018

Cited by 17 publications

References 34 publications

COVID-19 as a Factor Influencing Air Quality? A City Study in China

COVID-19 as a Factor Influencing Air Quality? A City Study in China

Investigation of the Impact of Land-Use Distribution on PM2.5 in Weifang: Seasonal Variations

Air Pollution Characteristics and Health Risks in the Yangtze River Economic Belt, China during Winter

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