Effects of Natural and Anthropogenic Factors on Climate and Environment

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 23450

Special Issue Editors

Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Interests: atmospheric physics; atmospheric chemistry; atmospheric environment; atmospheric sounding; climate and environment effects
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Institute of Nature and Environmental Technology, Kanazawa University, Ishikawa, Kanazawa 920-1192, Kakumamachi, Japan
Interests: air pollution; public health; PM 2.5; environmental science and technology; analytical chemistry
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: climate modeling; emission modeling; atmospheric environment and health; numerical weather prediction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue “Effects of Natural and Anthropogenic Activities on the Climate and Environment” will focus on the natural and anthropogenic activities that affect the climate and environment of any region around the globe. The climate and environment of any region may be affected by natural/anthropogenic activities occurring at any place, for example, the emissions from a volcanic eruption may affect the climate and environment of another region through the occurrence of aerosol transportation under general circulation, the dust and aerosol from a desert may be transported to another region and cause health impacts, and the emissions from fossil fuels may affect the surrounding region, etc. The pollution of urban areas, the pollution from brick kilns and other activities in rural areas, and coastal or marine pollution may be transported to their surroundings and result in severe negative impacts on the climate and environment. The change in climate due to natural and anthropogenic activities may bring changes to the environment that, in turn, may provide a suitable environment for the growth and development of different water- and vectorborne diseases such as dengue and chikungunya, etc. Carbon emissions, as is well known, are associated with a greenhouse effect while also being very important for the climate and environment. The changing climate due to anthropogenic activities may bring changes in temperature, precipitation, and wind circulation, which may in turn cause a change in the biodiversity of any region. Atmospheric and water thermal extremes may also bring storms and cyclones, which may cause disaster in any region. This Special Issue will take a broader view and welcomes articles introducing new methods and techniques in environmental and climate modeling and simulation, numerical predictions, and thermal extremes, etc., for demonstrating the impacts of natural and anthropogenic activities on the climate and environment. Also welcome are articles that use significant and novel methods and present new solutions to problems arising from natural and anthropogenic activities.

Dr. Bin Chen
Prof. Dr. Ning Tang
Dr. Bushra Khalid
Guest Editors

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Keywords

  • effect of anthropogenic emissions on climate and environment
  • effect of natural activities/emissions on climate and environment
  • carbon emission and carbon neutrality
  • changes in general circulation and its impacts on climate and environment
  • urban climate and environment
  • rural climate and environment
  • coastal and marine climate and environment
  • mountainous climate and environment
  • numerical and statistical modeling
  • thermal extremes and its impact on climate and environment
  • sea level rise
  • water extremes

Published Papers (11 papers)

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Research

14 pages, 716 KiB  
Article
The Relationship between Ambient Fine Particulate Matter (PM2.5) Pollution and Depression: An Analysis of Data from 185 Countries
by Ravi Philip Rajkumar
Atmosphere 2023, 14(3), 597; https://doi.org/10.3390/atmos14030597 - 21 Mar 2023
Cited by 4 | Viewed by 1952
Abstract
Several studies have identified a relationship between air pollution and depression, particularly in relation to fine particulate matter (PM2.5) exposure. However, the strength of this association appears to be moderated by variables such as age, gender, genetic vulnerability, physical activity, and [...] Read more.
Several studies have identified a relationship between air pollution and depression, particularly in relation to fine particulate matter (PM2.5) exposure. However, the strength of this association appears to be moderated by variables such as age, gender, genetic vulnerability, physical activity, and climatic conditions, and has not been assessed at a cross-national level to date. Moreover, certain studies in this field have yielded negative results, and there are discrepancies between the results obtained in high-income countries and those from low- and middle-income countries. The current study examines cross-sectional and longitudinal associations between the incidence of depression in each country, based on Global Burden of Disease Study data, and the average national level of PM2.5 based on the World Health Organization’s database, over the past decade (2010–2019). The observed associations were adjusted for age, gender, level of physical activity, income, education, population density, climate, and type of depression. It was observed that while PM2.5 levels showed significant cross-sectional associations with the incidence of depression, longitudinal analyses were not suggestive of a direct causal relationship. These findings are discussed in the light of recent contradictory results in this field, and the need to consider the intermediate roles of a number of individual and environmental factors. Full article
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19 pages, 3388 KiB  
Article
The Distribution and Impact Characteristics of Small-Scale Carbon Emissions in the Chengdu–Chongqing Region
by Xin Chen, Jialing Qin, Jian Yao, Zhishan Yang and Xuedong Li
Atmosphere 2023, 14(2), 216; https://doi.org/10.3390/atmos14020216 - 19 Jan 2023
Cited by 2 | Viewed by 1668
Abstract
In order to realize low-carbon and high-quality development, this study took the carbon emissions of each district and county in the Chengdu–Chongqing region from 2005 to 2017 as the research object and used the spatial autocorrelation model to analyze the spatial and temporal [...] Read more.
In order to realize low-carbon and high-quality development, this study took the carbon emissions of each district and county in the Chengdu–Chongqing region from 2005 to 2017 as the research object and used the spatial autocorrelation model to analyze the spatial and temporal evolution characteristics of carbon emissions in the counties of the Chengdu–Chongqing region, so as to fill in the research blank of carbon emissions in the counties of the Chengdu–Chongqing region. Then, the geographical detector model is used to explore the interaction among influencing factors of carbon emissions and reveal the time changes and regional differences of influencing factors, so as to improve the lack of spatial and temporal heterogeneity of influencing factors of carbon emissions by geographical detector. The results show the following: (i) The overall carbon emissions of counties show a year-on-year growth trend with the main urban areas of Chengdu and Chongqing as the core, but the growth rate slows down after 2010. (ii) The carbon emissions showed a significant positive spatial autocorrelation, and the neighboring counties showed a spatial clustering characteristic of “high-high” or “low-low”, and the clustering status tended to be enhanced. (iii) The carbon emissions are strongly influenced by industrial structure, economic development, investment level, financial situation, urbanization rate and social consumption, and their interactions are all enhanced, but the influence mostly tends to rise first and then fall. (iv) County carbon emissions can be divided into four types of geographical types, such as population size influencing type, urbanization rate influencing type, economic development influencing type and industrial structure influencing type. Therefore, a variety of factors need to be considered comprehensively, a multi-pronged approach, and a comprehensive policy to realize low-carbon transformation in the Chengdu–Chongqing region. Full article
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15 pages, 3166 KiB  
Article
Pollution Levels for Airborne Hexavalent Chromium of PM2.5 in Typical Cities of China
by Luxi Wang, Jinghua Guo, Wenjie Zhang, Bin Chen, Han Wang and Hui Li
Atmosphere 2023, 14(2), 209; https://doi.org/10.3390/atmos14020209 - 19 Jan 2023
Cited by 1 | Viewed by 1634
Abstract
As a carcinogenic pollutant, hexavalent chromium (Cr(VI)) in the ambient air has serious influences on public health. Due to its instability and difficulty in chemical analysis, few studies have been conducted on the regional concentration level of environmental Cr(VI), especially in China. The [...] Read more.
As a carcinogenic pollutant, hexavalent chromium (Cr(VI)) in the ambient air has serious influences on public health. Due to its instability and difficulty in chemical analysis, few studies have been conducted on the regional concentration level of environmental Cr(VI), especially in China. The pollution levels of Cr(VI) in ambient PM2.5 were determined in two sampling sites of Beijing and Qingdao from September to December 2020. The concentrations of Cr(VI) were determined using Ion Chromatography-inductively coupled plasma mass spectrometry(IC-ICP-MS), and the Cr concentrations were simultaneously analyzed by Inductively Coupled Plasma-atomic emission spectrometry(ICP-AES). The main results are as follows: (1) Based on the analysis of samples collected at the sampling sites of Beijing and Qingdao, the concentrations of Cr(VI) in PM2.5 were (0.140 ± 0.065) ng/m3 and (0.091 ± 0.073) ng/m3, respectively; (2) During the sampling period, the mean ratio of Cr(VI) to Cr was (0.0623 ± 0.0969). The ratio of Cr(VI)/Cr in Beijing and Qingdao were 0.076 ± 0.104 and 0.041 ± 0.039, respectively. In conclusion, compared with other countries, the concentration of Cr(VI) at the sampling sites of Beijing and Qingdao showed lower values. The average concentration of Cr(VI) estimated by the ratio of this research in China is lower than that in South Korea and similar to those in Canada and Australia. Full article
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11 pages, 2280 KiB  
Article
Effects of Precipitation Change and Nitrogen and Phosphorus Additions on Traits and Abundance of Potentilla anserina in an Alpine Meadow
by Lin Wu, Yanmei Ren, Ji-Zhong Wan, Mengyan Wang, Zuoyi Wang, Feiyan Fu, Jianping Sun, Yanjin Fu, Zhen Ma and Chunhui Zhang
Atmosphere 2022, 13(11), 1820; https://doi.org/10.3390/atmos13111820 - 2 Nov 2022
Cited by 3 | Viewed by 1493
Abstract
Changes in precipitation patterns and eutrophication can cause changes in plant traits and abundance, potentially affecting plant community structure and functions. Here, we studied responses of traits and abundance of Potentilla anserina to precipitation change and nitrogen (N) and phosphorus (P) additions, and [...] Read more.
Changes in precipitation patterns and eutrophication can cause changes in plant traits and abundance, potentially affecting plant community structure and functions. Here, we studied responses of traits and abundance of Potentilla anserina to precipitation change and nitrogen (N) and phosphorus (P) additions, and the effect of traits on its abundance in an alpine meadow of the Qinghai-Tibet Plateau. We found that precipitation change and N and P additions significantly affected the mean value of traits such as specific leaf area (SLA), leaf dry matter content (LDMC), single leaf area, plant height and individual size, while only P addition significantly affected intraspecific variation of SLA and individual size. Increased precipitation and N and P additions shifted plant traits to more resource acquisitive, and increased plant abundance. Responses of plant traits to P addition were larger than that of N addition. Plant abundance was mainly affected by precipitation, and was limited by N or P dependent on precipitation conditions. In conclusions, our research shows that P. anserina can respond to environmental changes by changing its traits to improve its adaptability, potentially affecting community structure and ecosystem functions. Full article
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14 pages, 3550 KiB  
Article
Cross-Inventory Uncertainty Analysis of Fossil Fuel CO2 Emissions for Prefecture-Level Cities in Shandong Province
by Mengchu Tao, Zhaonan Cai, Ke Che, Yi Liu, Dongxu Yang, Lin Wu, Pucai Wang and Mingzhu Yang
Atmosphere 2022, 13(9), 1474; https://doi.org/10.3390/atmos13091474 - 10 Sep 2022
Cited by 2 | Viewed by 1706
Abstract
A series of carbon dioxide (CO2) emission inventories with high spatial resolutions covering China have been developed in the last decade, making it possible to assess not only the anthropogenic emissions of large administrational units (countries; provinces) but also those of [...] Read more.
A series of carbon dioxide (CO2) emission inventories with high spatial resolutions covering China have been developed in the last decade, making it possible to assess not only the anthropogenic emissions of large administrational units (countries; provinces) but also those of small administrational units (cities; counties). In this study, we investigate three open-source gridded CO2 emission inventories (EDGAR; MEIC; PKU-CO2) and two statistical data-based inventories (CHRED; CEADs) covering the period of 2000–2020 for 16 prefecture-level cities in Shandong province in order to quantify the cross-inventory uncertainty and to discuss potential reasons for it. Despite ±20% differences in aggregated provincial emissions, all inventories agree that the emissions from Shandong increased by ~10% per year before 2012 and that the increasing trend slowed down after 2012, with a quasi-stationary industrial emission proportion being observed during 2008–2014. The cross-inventory discrepancies increased remarkably when downscaled to the city level. The relative differences between two individual inventories for half of the cities exceeded 100%. Despite close estimations of aggregated provincial emissions, the MEIC provides relatively high estimates for cities with complex and dynamic industrial systems, while the CHRED tends to provide high estimates for heavily industrial cities. The CHRED and MEIC show reasonable agreement regarding the evolution of city-level emissions and the city-level industrial emission ratios over 2005–2020. The PKU-CO2 and EDGAR failed to capture the emissions and their structural changes at the city level, which is related to their point-source database stopping updates after 2012. Our results suggest that cross-inventory differences for city-level emissions exist not only in their aggregated emissions but also in their changes over time. Full article
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14 pages, 2225 KiB  
Article
Abundance, Source Apportionment and Health Risk Assessment of Polycyclic Aromatic Hydrocarbons and Nitro-Polycyclic Aromatic Hydrocarbons in PM2.5 in the Urban Atmosphere of Singapore
by Yan Wang, Hao Zhang, Xuan Zhang, Pengchu Bai, Lulu Zhang, Sim Joo Huang, Stephen Brian Pointing, Seiya Nagao, Bin Chen, Akira Toriba and Ning Tang
Atmosphere 2022, 13(9), 1420; https://doi.org/10.3390/atmos13091420 - 2 Sep 2022
Cited by 6 | Viewed by 2125
Abstract
In this study, the levels of fine particulate matter (PM2.5), polycyclic aromatic hydrocarbons (PAHs) and nitro-PAHs (NPAHs) in PM2.5 samples were determined from 2020 to 2021 in Singapore. For analysis convenience, the sampling period was classified according to two monsoon [...] Read more.
In this study, the levels of fine particulate matter (PM2.5), polycyclic aromatic hydrocarbons (PAHs) and nitro-PAHs (NPAHs) in PM2.5 samples were determined from 2020 to 2021 in Singapore. For analysis convenience, the sampling period was classified according to two monsoon periods and the inter-monsoon period. Considering Singapore’s typically tropical monsoon climate, the four seasons were divided into the northeast monsoon season (NE), southwest monsoon season (SW), presouthwest monsoon season (PSW) and prenortheast monsoon season (PNE)). The PM2.5 concentration reached 17.1 ± 8.38 μg/m3, which was slightly higher than that in 2015, and the average PAH concentration continuously declined during the sampling period compared to that reported in previous studies in 2006 and 2015. This is the first report of NPAHs in Singapore indicating a concentration of 13.1 ± 10.7 pg/m3. The seasonal variation in the PAH and NPAH concentrations in PM2.5 did not obviously differ owing to the unique geographical location and almost uniform climate changes in Singapore. Diagnostic ratios revealed that PAHs and NPAHs mainly originated from local vehicle emissions during all seasons. 2-Nitropyrene (2-NP) and 2-nitrofluoranthene (2-NFR) in Singapore were mainly formed under the daytime OH-initiated reaction pathway. Combined with airmass backward trajectory analysis, the Indonesia air mass could have influenced Singapore’s air pollution levels in PSW. However, these survey results showed that no effect was found on the concentrations of PAHs and NPAHs in PM2.5 in Indonesia during SW because of Indonesia’s efforts in the environment. It is worth noting that air masses from southern China could impact the PAH and NPAH concentrations according to long-range transportation during the NE. The results of the total incremental lifetime cancer risk (ILCR) via three exposure routes (ingestion, inhalation and dermal absorption) for males and females during the four seasons indicated a low long-term potential carcinogenic risk, with values ranging from 10−10 to 10−7. This study systematically explains the latest pollution conditions, sources, and potential health risks in Singapore, and comprehensively analyses the impact of the tropical monsoon system on air pollution in Singapore, providing a new perspective on the transmission mechanism of global air pollution. Full article
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16 pages, 2458 KiB  
Article
Chemical Characteristics of Water-Soluble Inorganic Ions in Different Types of Asian Dust in Wajima, a Background Site in Japan
by Pengchu Bai, Hao Zhang, Xuan Zhang, Yan Wang, Lulu Zhang, Seiya Nagao, Bin Chen and Ning Tang
Atmosphere 2022, 13(8), 1210; https://doi.org/10.3390/atmos13081210 - 1 Aug 2022
Cited by 1 | Viewed by 1558
Abstract
Two Asian dust (AD) events were observed in March 2021 (AD1: 16 March 2021 00:00 UTC~17 March 2021 12:00 UTC and AD2: 28 March 2021 00:00 UTC~31 March 2021 12:00 UTC). To determine the chemical characteristics of water-soluble inorganic ions (WSIIs) in different [...] Read more.
Two Asian dust (AD) events were observed in March 2021 (AD1: 16 March 2021 00:00 UTC~17 March 2021 12:00 UTC and AD2: 28 March 2021 00:00 UTC~31 March 2021 12:00 UTC). To determine the chemical characteristics of water-soluble inorganic ions (WSIIs) in different types of Asian dust, the total suspended particulates (TSP) were collected at Kanazawa University Wajima Air Monitoring Station (KUWAMS), a background site in Japan from 27 February to 4 March, 2021. Based on the lidar observations and the backwards trajectory analysis results, AD events were divided into two types: ADN (aerosols were mainly mineral dust) and ADP (aerosols were mixtures of spherical particles). During ADs, the concentrations of the TSP and WSII increased, with the highest TSP concentration in ADN (38.6 μg/m3) and the highest WSII concentration in ADP (5.82 μg/m3). The increase in (cations)/(anions) during AD indicates that the input of AD aerosol buffered the aerosol acidity. Additionally, a significant increase in Cl depletion, along with ADN events, was found (Cl depletion = 73.8%). To comprehensively analyse the different types of ADs on WSIIs, we refer to the previous data from 2010 to 2015 at KUWAMS. As a result, the increased Cl depletion was caused by the heterogeneous reaction of HNO3 with sea salt when the air mass passed over the Japanese Sea. Additionally, the chemical form of SO42− was highly dependent on the source and pathway, while SO42− mainly came from natural soil dust in ADN and from anthropogenic emissions in ADP. The enhancement of secondary NO3 was observed in AD via the heterogeneous hydrolysis of N2O5. Full article
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18 pages, 6482 KiB  
Article
Variations in Aerosol Optical Properties over East Asian Dust Storm Source Regions and Their Climatic Factors during 2000–2021
by Saichun Tan, Bin Chen, Hong Wang, Huizheng Che, Huaying Yu and Guangyu Shi
Atmosphere 2022, 13(6), 992; https://doi.org/10.3390/atmos13060992 - 20 Jun 2022
Cited by 4 | Viewed by 1966
Abstract
The East Asian dust storms occur in western and northern China, and southern Mongolia every year, particularly in spring. In this study, we use satellite aerosol products to demonstrate the spatial and temporal variation in aerosol optical depth (AOD) from MODIS, and the [...] Read more.
The East Asian dust storms occur in western and northern China, and southern Mongolia every year, particularly in spring. In this study, we use satellite aerosol products to demonstrate the spatial and temporal variation in aerosol optical depth (AOD) from MODIS, and the absorbing aerosol index (AAI) from TOMS and OMI, over the main dust storm source regions (MDSR), and to investigate their relationship to vegetation coverage (NDVI), soil properties (surface soil moisture content and soil temperature 0–10 cm underground), and climatic factors (surface wind speed, air temperature at 2 m above the ground, and precipitation) in spring for the period of 2000–2021. Compared with dust storm occurrence frequency (DSF) observed at surface stations, MODIS AOD, TOMS AAI, and OMI AAI showed consistent spatial distributions and seasonal variations with DSF in the MDSR, with correlation coefficients of 0.88, 0.55, and 0.88, respectively. The results showed that AOD and AAI over the MDSR decreased during 2000–2005, 2006–2017, and 2000–2021, but increased during 2017–2021.The improvements in vegetation coverage and soil moisture together with favorable climatic factors (the increase in temperature and precipitation and the decrease in surface wind speed) resulted in the decreasing trend of AOD and AAI during 2000–2005, 2006–2017, and the entire period of 2000–2021. Conversely, the increase in surface wind speed, the decrease in temperature and the low soil moisture in 2018 and 2020 were the reasons for the increases in AOD and AAI over the MDSR during 2017–2021. The combination effects of surface wind, temperature, soil moisture, and vegetation coverage would determine DSF, AOD, and AAI, in the end, under global climate change. Full article
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17 pages, 2820 KiB  
Article
Greenhouse Gas Emissions Response to Fertilizer Application and Soil Moisture in Dry Agricultural Uplands of Central Kenya
by Peter Semba Mosongo, David E. Pelster, Xiaoxin Li, Gokul Gaudel, Yuying Wang, Suying Chen, Wenyan Li, David Mburu and Chunsheng Hu
Atmosphere 2022, 13(3), 463; https://doi.org/10.3390/atmos13030463 - 13 Mar 2022
Cited by 5 | Viewed by 2859
Abstract
In sub-Saharan Africa, agriculture can account for up to 66% of anthropogenic greenhouse gas (GHG) emissions. Unfortunately, due to the low number of studies in the region there is still much uncertainty on how management activities can affect these emissions. To help reduce [...] Read more.
In sub-Saharan Africa, agriculture can account for up to 66% of anthropogenic greenhouse gas (GHG) emissions. Unfortunately, due to the low number of studies in the region there is still much uncertainty on how management activities can affect these emissions. To help reduce this uncertainty, we measured GHG emissions from three maize (Zea mays) growing seasons in central Kenya. Treatments included: (1) a no N application control (C); (2) split (30% at planting and 70% 1 month after planting) mineral nitrogen (N) applications (Min—100 kg N ha−1); (3) split mineral N + irrigation (equivalent to 10 mm precipitation every three days—MI); (4) split mineral N + 40 kg N ha−1 added as manure (MM—total N = 140 kg ha−1); and (5) split mineral + intercropping with faba beans (Phaseolus vulgaris—MB). Soil CO2 fluxes were lower in season 1 compared to seasons 2 and 3 with fluxes highest in Min (p = 0.02) in season 2 and lowest in C (p = 0.02) in season 3. There was uptake of CH4 in these soils that decreased from season 1 to 3 as the mean soil moisture content increased. Cumulative N2O fluxes ranged from 0.25 to 2.45 kg N2O-N ha−1, with the highest fluxes from MI during season 3 (p = 0.01) and the lowest from C during season 1 (p = 0.03). The average fertilizer induced emission factor (0.36 ± 0.03%) was roughly one-third the default value of 1%. Soil moisture was a critical factor controlling GHG emissions in these central Kenya highlands. Under low soil moisture, the soils were CH4 sinks and minimal N2O sources. Full article
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18 pages, 5850 KiB  
Article
The Different Impacts of Emissions and Meteorology on PM2.5 Changes in Various Regions in China: A Case Study
by Wenjie Zhang, Hong Wang, Xiaoye Zhang, Yue Peng, Zhaodong Liu, Junting Zhong, Yaqiang Wang, Huizheng Che and Yifan Zhao
Atmosphere 2022, 13(2), 222; https://doi.org/10.3390/atmos13020222 - 28 Jan 2022
Cited by 2 | Viewed by 2559
Abstract
Emissions and meteorology are significant factors affecting aerosol pollution, but it is not sufficient to understand their relative contributions to aerosol pollution changes. In this study, the observational data and the chemical model (GRAPES_CUACE) are combined to estimate the drivers of PM2.5 [...] Read more.
Emissions and meteorology are significant factors affecting aerosol pollution, but it is not sufficient to understand their relative contributions to aerosol pollution changes. In this study, the observational data and the chemical model (GRAPES_CUACE) are combined to estimate the drivers of PM2.5 changes in various regions (the Beijing–Tianjin–Hebei (BTH), the Central China (CC), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD)) between the first month after COVID-19 (FMC_2020) (i.e., from 23 January to 23 February 2020) and the corresponding period in 2019 (FMC_2019). The results show that PM2.5 mass concentration increased by 26% (from 61 to 77 µg m−3) in the BTH, while it decreased by 26% (from 94 to 70 µg m−3) in the CC, 29% (from 52 to 37 µg m−3) in the YRD, and 32% (from 34 to 23 µg m−3) in the PRD in FMC_2020 comparing with FMC_2019, respectively. In the BTH, although emissions reductions partly improved PM2.5 pollution (−5%, i.e., PM2.5 mass concentration decreased by 5% due to emissions) in FMC_2020 compared with that of FMC_2019, the total increase in PM2.5 mass concentration was dominated by more unfavorable meteorological conditions (+31%, i.e., PM2.5 mass concentration increased by 31% due to meteorology). In the CC and the YRD, emissions reductions (−33 and −36%) played a dominating role in the total decrease in PM2.5 in FMC_2020, while the changed meteorological conditions partly worsened PM2.5 pollution (+7 and +7%). In the PRD, emissions reductions (−23%) and more favorable meteorological conditions (−9%) led to a total decrease in PM2.5 mass concentration. This study reminds us that the uncertainties of relative contributions of meteorological conditions and emissions on PM2.5 changes in various regions are large, which is conducive to policymaking scientifically in China. Full article
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17 pages, 5737 KiB  
Article
Multiple Regression Analysis of Low Visibility Focusing on Severe Haze-Fog Pollution in Various Regions of China
by Zhaodong Liu, Hong Wang, Yue Peng, Wenjie Zhang and Mengchu Zhao
Atmosphere 2022, 13(2), 203; https://doi.org/10.3390/atmos13020203 - 27 Jan 2022
Cited by 7 | Viewed by 2482
Abstract
Visibility degradation is a pervasive environmental problem in winter in China and its prediction accuracy is therefore important, especially in low visibility conditions. However, current visibility parameterization algorithms tend to overestimate low visibility (<5 km) during haze–fog events. The key point of low [...] Read more.
Visibility degradation is a pervasive environmental problem in winter in China and its prediction accuracy is therefore important, especially in low visibility conditions. However, current visibility parameterization algorithms tend to overestimate low visibility (<5 km) during haze–fog events. The key point of low visibility calculation and prediction depends on a reasonable understanding of the correlation between visibility, PM2.5 concentration, and relative humidity (RH). Using the observations of PM2.5 concentration and meteorology from December 2016 to February 2017, under different RH levels, the relative contribution differences of PM2.5 concentrations and RH to visibility degradation are investigated in depth. On this basis, new multiple nonlinear regressions for low visibility are developed for eight regions of China. The results show that under relatively low RH conditions (<80% or 85%), PM2.5 concentration plays a leading role in visibility changes in China. With the increase in RH (80–90% or 85–95%), the PM2.5 concentration corresponding to the visibility of 10 and 5 km decreases and the contribution of RH becomes increasingly important. When the RH grows to >95%, a relatively low PM2.5 concentration could also lead to visibility decreasing to <5 km. Within this range, the PM2.5 concentration corresponding to the visibility of 5 km in Central China (CC), Sichuan Basin (SCB), and Yangtze River Delta (YRD) is approximately 50, 50, and 30 μg m−3, and that in Beijing-Tianjin-Hebei (BTH) and Guanzhong Plain (GZP) is approximately 125 μg m−3, respectively. Specifically, based on these contribution differences, new multiple nonlinear regression equations of visibility, PM2.5 concentration, temperature, and dew point temperature of the eight regions (Scheme A) are established respectively after grouping the datasets by setting different RH levels (BTH, GZP, and North Eastern China (NEC): RH < 80%, 80 ≤ RH < 90% and RH ≥ 90%; CC, SCB, YRD, and South China Coastal (SCC): RH < 85%, 85 ≤ RH < 95% and RH ≥ 95%; Xinjiang (XJ): RH < 90% and RH ≥ 90%). According to the previous regression methods, we directly established the multiple regression models between visibility and the same factors as a comparison (Scheme B). Statistical results show that the advantage of Scheme A for 5 and 3 km evaluation is more significant compared with Scheme B. For the five low visibility regions (BTH, GZP, CC, SCB, and YRD), RMSEs of Scheme A under visibility <5 and 3 km are 0.77–1.01 and 0.48–0.95 km, 16–43 and 24–57% lower than those of Scheme B, respectively. Moreover, Scheme A reproduced the winter visibility in BTH, GZP, CC, SCB, YRD, and SCC from 2016 to 2020 well. The MAEs, MBs, and RMSEs under visibility < 5 km are 0.44–1.41, −1.33–1.24, and 0.58–2.36 km, respectively. Overall, Scheme A is confirmed to be reliable and applicable for low visibility prediction in many regions of China. This study provides a new visibility parameterization algorithm for the haze–fog numerical prediction system. Full article
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