Characteristics and sources of atmospheric pollutants in typical inland cities in arid regions of central Asia: A case study of Urumqi city

The arid zone of central Asia secluded inland and has the typical features of the atmosphere. Human activities have had a significant impact on the air quality in this region. Urumqi is a key city in the core area of the Silk Road and an important economic center in Northwestern China. The urban environment is playing an increasingly important role in regional development. To study the characteristics and influencing factors of the main atmospheric pollutants in Urumqi, this study selected Urumqi’s daily air quality index (AQI) data and observation data of six major pollutants including fine particulate matter (PM2.5), breathable particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3_8h) from 2014 to 2018 in conjunction with meteorological data to use a backward trajectory analysis method to study the main characteristics of atmospheric pollutants and their sources in Urumqi from 2014 to 2018. The results showed that: (1) From 2014 to 2018, the annual average of PM2.5, PM10, SO2, NO2 and CO concentrations showed a downward trend, and O3_8h concentrations first increased, then decreased, and then increased, reaching the highest value in 2018 (82.15 μg·m-3); The seasonal changes of PM2.5, PM10, SO2, NO2 and CO concentrations were characterized by low values in summer and fall seasons and high values in winter and spring seasons. The concentration of O3_8h, however, was in the opposite trend, showing the high values in summer and fall seasons, and low values in winter and spring seasons. From 2014 to 2018, with the exception of O3_8h, the concentration changes of the other five major air pollutants were high in December, January, and February, and low in May, June, and July; the daily changes showed a “U-shaped” change during the year. The high-value areas of the "U-shaped" mode formed around the 50th day and the 350th day. (2) The high-value area of AQI was from the end of fall (November) to the beginning of the following spring (March), and the low-value area was from April to October. It showed a U-shaped change trend during the year and the value was mainly distributed between 50 and 100. (3) The concentrations of major air pollutants in Urumqi were significantly negatively correlated with precipitation, temperature, and humidity (P<0.01), and had the highest correlation coefficients with temperature. (4) Based on the above analysis results, this study analyzed two severe pollution events from late November to early December. Analysis showed that the PM2.5/PM10 ratio in two events remained at about 0.1 when the pollution occurred, but was higher before and after the pollution (up to 1.46). It was shown that the pollution was a simple sandstorm process. Backward trajectory analysis clustered the airflow trajectories reaching Urumqi into 4 categories, and the trajectories from central Asia contributed the maximum values of average PM2.5 and PM10 concentrations.

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Roadside Air Quality Forecasting in Shanghai with a Novel Sequence-to-Sequence Model

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17249471 ◽

2020 ◽

Vol 17 (24) ◽

pp. 9471

Author(s):

Dongsheng Wang ◽

Hong-Wei Wang ◽

Chao Li ◽

Kai-Fa Lu ◽

Zhong-Ren Peng ◽

...

Keyword(s):

Air Quality ◽

Air Pollutants ◽

Traffic Management ◽

Fine Particulate Matter ◽

Temporal Variations ◽

Learning Ability ◽

Observation Data ◽

Deterministic Models ◽

Co Concentrations ◽

Air Quality Forecasting

The establishment of an effective roadside air quality forecasting model provides important information for proper traffic management to mitigate severe pollution, and for alerting resident’s outdoor plans to minimize exposure. Current deterministic models rely on numerical simulation and the tuning of parameters, and empirical models present powerful learning ability but have not fully considered the temporal periodicity of air pollutants. In order to take the periodicity of pollutants into empirical air quality forecasting models, this study evaluates the temporal variations of air pollutants and develops a novel sequence to sequence model with weekly periodicity to forecast air quality. Two-year observation data from Shanghai roadside air quality monitoring stations are employed to support analyzing and modeling. The results conclude that the fine particulate matter (PM2.5) and carbon monoxide (CO) concentrations show obvious daily and weekly variations, and the temporal patterns are nearly consistent with the periodicity of traffic flow in Shanghai. Compared with PM2.5, the CO concentrations are more affected by traffic variation. The proposed model outperforms the baseline model in terms of accuracy, and presents a higher linear consistency in PM2.5 prediction and lower errors in CO prediction. This study could assist environmental researchers to further improve the technologies for urban air quality forecasting, and serve as tools for supporting policymakers to implement related traffic management and emission control policies.

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A Review of Air Quality and Concentrations of Air Pollutants in Nigeria

Journal of Applied Sciences and Environmental Management ◽

10.4314/jasem.v24i2.25 ◽

2020 ◽

Vol 24 (2) ◽

pp. 373-379

Author(s):

I.B. Abaje ◽

Y. Bello ◽

S.A. Ahmad

Keyword(s):

Carbon Monoxide ◽

Particulate Matter ◽

Air Quality ◽

Air Pollutants ◽

Fine Particles ◽

Fine Fraction ◽

The Body ◽

Atmospheric Pollutants ◽

Anthropogenic Sources ◽

Waste Dumps

This study generally classifies air pollutants on the basis of: primary or secondary, natural or anthropogenic, chemical composition, physical state, and the space scales of their effects. Air pollutants that affect air quality in Nigeria were discussed based on natural and anthropogenic sources. The natural sources include: sand dust, sea spray, volcanic activities, smoke and carbon monoxide from wildfires among others, while the anthropogenic sources include: vehicular emissions, mining activities, industries such as cement companies and quarry factories, agricultural practices and solid waste dumps among others. Some of the atmospheric pollutants that posed greatest threat to human health were equally examined. They include: Sulphur dioxide (SO2) which can react with water vapor (H2O) in the atmosphere to form sulphuric acid (H2SO4) and thus acid rain; particulate matter (PM) with less than 10 μm, particularly fine particles (PM2.5 ) and particles in the fine fraction that are smaller than 0.1 μm (ultrafine particles), can carry toxic chemicals which are linked to cancer; carbon monoxide (CO), even in very small concentrations, can prevent oxygen from being delivered through the body major organs; ozone which is a highly reactive gas causes oxidation of a number of macromolecules within a biological system and produces free radicals that can damage DNA molecules and cause carcinogenesis. Based on the aforementioned, this study recommends that priority should be given to the establishment of air monitoring stations in all urban centers of the country in order to provide accurate and continuous information on air quality. Keywords: anthropogenic pollutants, atmosphere, particulate matter, pollution

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Three-Year Variations in Criteria Atmospheric Pollutants and Their Relationship with Rainwater Chemistry in Karst Urban Region, Southwest China

Atmosphere ◽

10.3390/atmos12081073 ◽

2021 ◽

Vol 12 (8) ◽

pp. 1073

Author(s):

Jie Zeng ◽

Xin Ge ◽

Qixin Wu ◽

Shitong Zhang

Keyword(s):

Particulate Matter ◽

Air Quality ◽

Seasonal Variations ◽

Air Pollutants ◽

Air Pollutant ◽

Atmospheric Pollutants ◽

Emission Sources ◽

Rainwater Chemistry ◽

Pollutant Concentrations ◽

Air Pollutant Concentrations

Air pollutants have been investigated in many studies, but the variations of atmospheric pollutants and their relationship with rainwater chemistry are not well studied. In the present study, the criteria atmospheric pollutants in nine monitoring stations and rainwater chemistry were analyzed in karst Guiyang city, since the time when the Chinese Ambient Air Quality Standards (CAAQS, third revision) were published. Based on the three-year daily concentration dataset of SO2, NO2, CO, PM10 and PM2.5, although most of air pollutant concentrations were within the limit of CAAQS III-Grade II standard, the significant spatial variations and relatively heavy pollution were found in downtown Guiyang. Temporally, the average concentrations of almost all air pollutants (except for CO) decreased during three years at all stations. Ratios of PM2.5/PM10 in non- and episode days reflected the different contributions of fine and coarse particles on particulate matter in Guiyang, which was influenced by the potential meteorological factors and source variations. According to the individual air quality index (IAQI), the seasonal variations of air quality level were observed, that is, IAQI values of air pollutants were higher in winter (worst air quality) and lower in summer (best air quality) due to seasonal variations in emission sources. The unique IAQI variations were found during the Chinese Spring Festival. Air pollutant concentrations are also influenced by meteorological parameters, in particular, the rainfall amount. The air pollutants are well scoured by the rainfall process and can significantly affect rainwater chemistry, such as SO42−, NO3−, Mg2+, and Ca2+, which further alters the acidification/alkalization trend of rainwater. The equivalent ratios of rainwater SO42−/NO3− and Mg2+/Ca2+ indicated the significant contribution of fixed emission sources (e.g., coal combustion) and carbonate weathering-influenced particulate matter on rainwater chemistry. These findings provide scientific support for air pollution management and rainwater chemistry-related environmental issues.

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Spatial and Temporal Characteristics of Environmental Air Quality and Its Relationship with Seasonal Climatic Conditions in Eastern China during 2015–2018

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18094524 ◽

2021 ◽

Vol 18 (9) ◽

pp. 4524

Author(s):

Zhiyuan Wang ◽

Xiaoyi Shi ◽

Chunhua Pan ◽

Sisi Wang

Keyword(s):

Particulate Matter ◽

Air Quality ◽

Air Pollutants ◽

Large Scale ◽

Asian Summer Monsoon ◽

Eastern China ◽

Climatic Conditions ◽

Uniform Structure ◽

Concentration Limit ◽

Environmental Air

Exploring the relationship between environmental air quality (EAQ) and climatic conditions on a large scale can help better understand the main distribution characteristics and the mechanisms of EAQ in China, which is significant for the implementation of policies of joint prevention and control of regional air pollution. In this study, we used the concentrations of six conventional air pollutants, i.e., carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2), fine particulate matter (PM2.5), coarse particulate matter (PM10), and ozone (O3), derived from about 1300 monitoring sites in eastern China (EC) from January 2015 to December 2018. Exploiting the grading concentration limit (GB3095-2012) of various pollutants in China, we also calculated the monthly average air quality index (AQI) in EC. The results show that, generally, the EAQ has improved in all seasons in EC from 2015 to 2018. In particular, the concentrations of conventional air pollutants, such as CO, SO2, and NO2, have been decreasing year by year. However, the concentrations of particulate matter, such as PM2.5 and PM10, have changed little, and the O3 concentration increased from 2015 to 2018. Empirical mode decomposition (EOF) was used to analyze the major patterns of AQI in EC. The first mode (EOF1) was characterized by a uniform structure in AQI over EC. These phenomena are due to the precipitation variability associated with the East Asian summer monsoon (EASM), referred to as the “summer–winter” pattern. The second EOF mode (EOF2) showed that the AQI over EC is a north–south dipole pattern, which is bound by the Qinling Mountains and Huaihe River (about 35° N). The EOF2 is mainly caused by seasonal variations of the mixed concentration of PM2.5 and O3. Associated with EOF2, the Mongolia–Siberian High influences the AQI variation over northern EC by dominating the low-level winds (10 m and 850 hPa) in autumn and winter, and precipitation affects the AQI variation over southern EC in spring and summer.

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Ensemble Predictions of Air Pollutants in China in 2013 for Health Effects Studies Using WRF/CMAQ Modeling System with Four Emission Inventories

10.5194/acp-2017-182 ◽

2017 ◽

Cited By ~ 1

Author(s):

Jianlin Hu ◽

Xun Li ◽

Lin Huang ◽

Qi Ying ◽

Qiang Zhang ◽

...

Keyword(s):

Air Quality ◽

Health Effects ◽

Air Pollutants ◽

Emission Inventory ◽

Model Performance ◽

Observation Data ◽

Emission Inventories ◽

Air Quality Model ◽

Quality Model ◽

Future Health

Abstract. Accurate exposure estimates are required for health effects analyses of severe air pollution in China. Chemical transport models (CTMs) are widely used tools to provide detailed information of spatial distribution, chemical composition, particle size fractions, and source origins of pollutants. The accuracy of CTMs' predictions in China is largely affected by the uncertainties of public available emission inventories. The Community Multi-scale Air Quality model (CMAQ) with meteorological inputs from the Weather Research and Forecasting model (WRF) were used in this study to simulate air quality in China in 2013. Four sets of simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC), the Emission Inventory for China by School of Environment at Tsinghua University (SOE), the Emissions Database for Global Atmospheric Research (EDGAR), and the Regional Emission inventory in Asia version 2 (REAS2). Model performance was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O3 and PM2.5 with the four inventories generally meet the criteria of model performance, but difference exists in different pollutants and different regions among the inventories. Ensemble predictions were calculated by linearly combining the results from different inventories under the constraint that sum of the squared errors between the ensemble results and the observations from all the cities was minimized. The ensemble annual concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB) and mean fractional errors (MFE) of the ensemble predicted annual PM2.5 at the 60 cities are −0.11 and 0.24, respectively, which are better than the MFB (−0.25–−0.16) and MFE (0.26–0.31) of individual simulations. The ensemble annual 1-hour peak O3 (O3-1 h) concentrations are also improved, with mean normalized bias (MNB) of 0.03 and mean normalized errors (MNE) of 0.14, compared to MNB of 0.06–0.19 and MNE of 0.16–0.22 of the individual predictions. The ensemble predictions agree better with observations with daily, monthly, and annual averaging times in all regions of China for both PM2.5 and O3-1 h. The study demonstrates that ensemble predictions by combining predictions from individual emission inventories can improve the accuracy of predicted temporal and spatial distributions of air pollutants. This study is the first ensemble model study in China using multiple emission inventories and the results are publicly available for future health effects studies.

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Occupational exposure to particles and air pollutants in a car paint workshop

European Journal of Public Health ◽

10.1093/eurpub/ckab120.051 ◽

2021 ◽

Vol 31 (Supplement_2) ◽

Author(s):

Ana Ferreira ◽

António Loureiro ◽

Silvia Seco ◽

João Paulo Figueiredo ◽

Susana Paixão ◽

...

Keyword(s):

Carbon Dioxide ◽

Air Quality ◽

Occupational Exposure ◽

Occupational Health ◽

Air Pollutants ◽

Statistical Tests ◽

Well Being ◽

Atmospheric Pollutants ◽

Inhalable Particles ◽

The Government

Abstract Background In auto paint workshops there are several chemical, physical and biological agents that are harmful to health, making it essential to guarantee the well-being and safety of workers. In this sense, the assessment of the Indoor Air Quality (IAQ) of these places, in an associated context of occupational health, proves to be important. Methods The present study had as main objective to evaluate the occupational exposure of workers in an automobile painting workshop to particles and air pollutants. The data collection consisted of the evaluation of air quality, using for this purpose, the assessment of atmospheric pollutants carbon monoxide (CO), carbon dioxide (CO2), Volatile Organic Compounds (VOC), formaldehyde (CH2O), carbon dioxide sulfur (SO2), nitrogen dioxide (NO2), hydrogen sulfide (H2S) and particulate matter (PM2.5, PM10, breathable and inhalable particles) and the meteorological variables temperature and relative humidity. The collected data was processed using the statistical software IBM SPSS version 27.0. The interpretation of the statistical tests was performed with a 95% confidence level for a maximum random error up to 5%. Results We found that the concentrations of inhalable particles recorded in some workstations exceeded the legally established exposure limit value. Conclusions IAQ should be a priority concern for the government and for all professionals working in the area of Occupational Health and Safety having in mind the implementation of measures that promote the continuous improvement of the IAQ of the facilities, thus guaranteeing a good assessment and monitoring of workstations, preventing atmospheric pollutants from reaching concentrations that could put workers' health at risk.

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Impacts of emission changes in China from 2010 to 2017 on domestic and intercontinental air quality and health effect

10.5194/acp-2021-385 ◽

2021 ◽

Author(s):

Yuqiang Zhang ◽

Drew Shindell ◽

Karl Seltzer ◽

Lu Shen ◽

Jean-Francois Lamarque ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Air Pollutants ◽

Transport Model ◽

Surface Ozone ◽

Health Effect ◽

Premature Deaths ◽

Concentration Changes ◽

Emission Changes ◽

Related Mortality

Abstract. China has seen dramatic emission changes from 2010, especially after the implementation of Clean Air Action in 2013, with significant air quality and human health benefits observed. Air pollutants, such as PM2.5 and surface ozone, as well as their precursors, have long enough lifetime in the troposphere which can be easily transported downwind. So emission changes in China will not only change the regional air quality domestically, but also affect the air quality in downwind regions. In this study, we use a global chemistry transport model to simulate the influence on both domestic and foreign air quality from the emission change from 2010 to 2017 in China. By applying the health impact functions derived from epidemiology studies, we then quantify the changes in air pollution-related (including both PM2.5 and O3) mortality burdens at regional and global scales. The majority of air pollutants in China reach their peak values around 2012 and 2013. Compared with the year 2010, the population-weighted annual PM2.5 in China increases till 2011 (94.1 μg m−3), and then begins to decrease. In 2017, the population-weighted annual PM2.5 decreases by 17.6 %, compared with the values in 2010 (84.7 μg m−3). The estimated national PM2.5 concentration changes in China are comparable with previous studies using fine-resolution regional models, though our model tends to overestimate PM2.5 from 2013 to 2017 when evaluated with surface observation in China during the same periods. The emission changes in China increased the global PM2.5-related mortality burdens from 2010 to 2013, by 27,700 (95 %CI: 23,900–31, 400) deaths yr−1 in 2011, and 13, 300 (11,400–15,100) deaths yr−1 in 2013, among which at least 93 % occurred in China. The sharp emission decreases after 2013 bring significant benefits for reduced avoided premature mortality in 2017, reaching 108, 800 (92,800–124,800) deaths yr−1 globally, among which 92 % happening in China. Different trend as PM2.5, the annual maximum daily 8-hr ozone in China increased, and also the ozone-related premature deaths, ranging from 3,600 (2,700–4,300) deaths yr−1 in 2011 (75 % of global total increased premature deaths), and 8,500 (6,500–9,900) deaths yr−1 in 2017 (143 % of the global total). Downwind regions, such as South Korea, Japan, and U.S. generally see a decreased O3-related mortality burden after 2013 as a combination of increased export of ozone and decreased export of ozone precursors. In general, we conclude that the sharp emission reductions in China after 2013 bring benefits of improved air quality and reduced premature deaths associated with air pollution at global scale. The benefits are dominated by the PM2.5 decreases since the ozone is shown to actually increase with the emission decrease.

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Air Quality in Riga and Its Improvement Options

Scientific Journal of Riga Technical University Environmental and Climate Technologies ◽

10.2478/v10145-011-0030-2 ◽

2011 ◽

Vol 7 (-1) ◽

pp. 72-78

Author(s):

Janis Kleperis ◽

Gunars Bajars ◽

Ingrida Bremere ◽

Martins Menniks ◽

Arturs Viksna ◽

...

Keyword(s):

Air Pollution ◽

Energy Efficiency ◽

Particulate Matter ◽

Air Quality ◽

European Commission ◽

Air Pollutants ◽

Transport Sector ◽

Industrial Sectors ◽

Particle Pollution ◽

The City

Air Quality in Riga and Its Improvement Options Air quality in the city of Riga is evaluated from direct monitoring results and from accounting registered air pollutants in the city. It is concluded that from all air polluting substances listed in the European Commission directives, only nitrogen dioxide NO2 and particulate matter PM10 exceed the limits. In assessing the projected measures to improve air quality in Riga, it can be concluded that the implementation of cleaner fuels and improvements in energy efficiency of household and industrial sectors will decrease particle pollution, but measures in the transport sector will also contribute to reducing air pollution from nitrogen oxides.

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Satellite-based observations of ground-level fine particulate matter and comparison to a regional air quality model

10.5194/egusphere-egu2020-10342 ◽

2020 ◽

Author(s):

Jana Handschuh ◽

Frank Baier ◽

Thilo Erbertseder ◽

Martijn Schaap

Keyword(s):

Particulate Matter ◽

Air Quality ◽

Air Pollutants ◽

Fine Particulate Matter ◽

Ground Level ◽

In Situ Measurements ◽

Satellite Observations ◽

Optical Parameters ◽

Fine Particulate

Particulate matter and other air pollutants have become an increasing burden on the environment and human health. Especially in metropolitan and high-traffic areas, air quality is often remarkably reduced. For a better understanding of the air quality in specific areas, which is of great environment-political interest, data with high resolution in space and time is required. The combination of satellite observations and chemistry-transport-modelling has proven to give a good database for assessments and analyses of air pollution. In contrast to sample in-situ measurements, satellite observations provide area-wide coverage &#8203;&#8203;of measurements and thus the possibility for an almost gapless mapping of actual air pollutants. For a high temporal resolution, chemistry-transport-models are needed, which calculate concentrations of specific pollutants in continuous time steps. Satellite observations can thus be used to improve model performances.There are no direct satellite-measurements of fine particulate matter (PM2.5) but ground-level concentrations of PM2.5 can be derived from optical parameters such as aerosol optical depth (AOD). A wide range of methods for the determination of PM2.5 concentrations from AOD measurements has been developed so far, but it is still a big challenge. In this study a semi-empirical approach based on the physical relationships between meteorological and optical parameters was applied to determine a first-guess of ground-level PM2.5 concentrations for the year 2018 and the larger Germany region. Therefor AOD observations of MODIS (Moderate Resolution Imaging Spectroradiometer) aboard the NASA Aqua satellite were used in a spatial resolution of 3km. First results showed an overestimation of ground-level aerosols and quiet low correlations with in-situ station measurements from the European Environmental Agency (EEA). To improve the results, correction factors were calculated using the coefficients of linear regression between satellite-based and in-situ measured particulate matter concentrations. Spatial and seasonal dependencies were taken into account with it. Correlations between satellite and in-situ measurements could be improved applying this method.The MODIS 3km AOD product was found to be a good base for area-wide calculations of ground-level PM2.5 concentrations. First comparisons to the calculated PM2.5 concentrations from chemistry-transport-model POLYPHEMUS/DLR showed significant differences though. Satellite observations will now be used to improve the general model performance, first by helping to find and understand regional and temporal dependencies in the differences. As part of the German project S-VELD funded by the Federal Ministry of Transport and Digital Infrastructure BMVI, it will help for example to adjust the derivation of particle emissions within the model.

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