scholarly journals Impact of COVID-19 on Extremely Polluted Air Quality and Trend Forecast in Seven Provinces and Three Cities of China

2021 ◽  
Vol 9 ◽  
Author(s):  
Xiaoying Pan ◽  
Yonggang Zhao ◽  
Meng Wang
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Average Concentration ◽  
Air Pollutant ◽  
Chinese Government ◽  
Annual Average ◽  
Epidemic Period ◽  
The World ◽  
Before And After ◽  
Annual Average Concentration

At the beginning of 2020, COVID-19 broke out. Because the virus is extremely contagious and the mortality rate after infection is extremely high, China and many countries in the world have imposed lockdowns. Air pollutants during the epidemic period have attracted the attention of many scholars. This research is to use predictive models to describe changes in extreme air pollutants. China is the first country in the world to enter the lockdown state. This study uses data from 2015-2020 to compare and predict the concentration of extreme pollutants before and after the lockdown. The results show that the lockdown of the epidemic will reduce the annual average concentration of PM2.5, and the annual average concentration of O3 will increase first and then decrease. Through analysis, it is concluded that there is a synergistic decrease trend between PM2.5 and O3. With the various blockade measures for epidemic prevention and control, the reduction of extreme air pollutant concentrations is sustainable. The assessment of China’s air quality in conjunction with the COVID-19 can provide scientific guidance for the Chinese government and other relevant departments to formulate policies.

Evaluation and Management of Ambient Air Quality, with Special Reference to China

1980 ◽  
Vol 7 (3) ◽  
pp. 223-228 ◽  
Author(s):  
Yao Zhi-Qi
Keyword(s):  
Air Quality ◽  
Geometric Mean ◽  
Monitoring And Evaluation ◽  
Ambient Air ◽  
Average Concentration ◽  
Quality Indices ◽  
Ambient Air Quality ◽  
Annual Average ◽  
Hygienic Standard ◽  
Annual Average Concentration

Monitoring and evaluation of air quality in urban and industrial areas are essential for air quality management. For evaluating the composite air-quality in the concomitant presence of several pollutants in the atmosphere, many air quality indices have been developed. This paper presents two indices, the ‘composite air-quality index (I1)’ and ‘the standard-exceeding index of air pollution (I2)’ together with their respective sub-indices, for the pollutants monitored and for use in combination.The first index, I1, is based on the annual average concentration measured in a year for each pollutant; it measures the overall composite air-quality. By relating the annual average concentration (Ci) of each pollutant to its hygienic standard (Si), as many (Ci/Si) values as the number of pollutant parameters monitored are found, whereupon I1 is computed as the geometric mean of the maximum and average of all (Ci/Si) values. A greater value of I1 means worse composite air-quality. It is simpler to compute than those more sophisticated ones in the literature, and holds the unique characteristic of considering, and yet not overemphasizing as formula (3) does (Nemerow, 1974), the maximum (Ci/Si) value.

scholarly journals The Temporal And Spatial Changes Of Beijing’s Pm 2.5 Concentration And Its Relationship With Meteorological Factors From 2015 To 2020

2021 ◽  
Vol 14 (3) ◽  
pp. 73-81
Author(s):  
Guo Peng ◽  
A. B. Umarova ◽  
G. S. Bykova
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Urban Areas ◽  
Meteorological Factors ◽  
Ambient Air ◽  
Average Concentration ◽  
Annual Average ◽  
Daily Average ◽  
Annual Average Concentration ◽  
Temporal And Spatial

Currently, Beijing is facing increasing serious air quality problems. Atmospheric pollutants in Beijing are mainly composed of particulate matter, which is a key factor leading to adverse effects on human health. This paper uses hourly data from 36 environmental monitoring stations in Beijing from 2015 to 2020 to obtain the temporal and spatial distribution of the mass concentration of particulate matter with a diameter smaller than 2.5 μm (PM2.5). The 36 stations established by the Ministry of Ecology and Environment and the Beijing Environmental Protection Monitoring Center and obtain continuous real-time monitoring of particulate matter. And the 36 stations are divided into 13 main urban environmental assessment points, 11 suburban assessment points, 1 control point, 6 district assessment points, and 5 traffic pollution monitoring points. The annual average concentration of PM2.5 in Beijing was 60 μg/m3 with a negative trend of approximately 14% year-1. In urban areas the annual average concentration of PM2.5 was 59 μg/m3, in suburbs 56 μg/m3, in traffic areas 63 μg/m3, and in district areas 62 μg/m3. From 2015 to 2020, in urban areas PM2.5 decreased by 14% year-1, in suburbs by 15% year -1, in traffic areas by 15% year-1, and in district areas by 12% year-1. The quarterly average concentrations of PM2.5 in winter andspring are higher than those in summer and autumn (64 μg/m3, 59 μg/m3, 45 μg/m3, 55 μg/m3, respectively). The influenceof meteorological factors on the daily average value of PM2.5 in each season was analysed. The daily average PM2.5 in spring, summer, autumn and winter is significantly negatively correlated with daily average wind speed, sunshine hours, and air pressure, and significantly positively correlated with daily average rainfall and relative humidity. Except for autumn, the daily average PM2.5 is positively correlated with temperature. Although Beijing’s PM2.5 has been declining since the adoption of the‘Air Pollution Prevention and Control Action Plan’, it is still far from the first level of the new ‘Ambient Air Quality Standard’(GB309S-2012) formulated by China in 2012.

scholarly journals Risk Assessment and Air Quality Study during Different Phases of COVID-19 Lockdown in an Urban Area of Klang Valley, Malaysia

2021 ◽  
Vol 13 (21) ◽  
pp. 12217
Author(s):  
Mohd Shahrul Mohd Nadzir ◽  
Mohd Zaim Mohd Nor ◽  
Mohd Fadzil Firdzaus Mohd Nor ◽  
Muhamad Ikram A Wahab ◽  
Sawal Hamid Md Ali ◽  
...  
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Positive Impact ◽  
Movement Control ◽  
Air Pollutant ◽  
Pollutant Concentrations ◽  
The World ◽  
Klang Valley ◽  
Air Pollutant Concentrations ◽  
Control Order

Globally, the COVID-19 pandemic has had both positive and negative impacts on humans and the environment. In general, a positive impact can be seen on the environment, especially in regard to air quality. This positive impact on air quality around the world is a result of movement control orders (MCO) or lockdowns, which were carried out to reduce the cases of COVID-19 around the world. Nevertheless, data on the effects on air quality both during and post lockdown at local scales are still sparse. Here, we investigate changes in air quality during normal days, the MCOs (MCO 1, 2 and 3) and post MCOs, namely the Conditional Movement Control Order (CMCO) and the Recovery Movement Control Order (RMCO) in the Klang Valley region. In this study, we used the air sensor network AiRBOXSense that measures carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2) and particulate matter (PM2.5 and PM10) at Petaling Jaya South (PJS), Kelana Jaya (KJ) and Kota Damansara (KD). The results showed that the daily average concentrations of CO and NO2 mostly decreased in the order of normal days > MCO (MCO 1, 2 and 3) > CMCO > RMCO. PM10, PM2.5, SO2 and O3 showed a decrease from the MCO to RMCO. PJS showed that air pollutant concentrations decreased from normal days to the lockdown phases. This clearly shows the effects of ‘work from home’ orders at all places in the PJS city. The greatest percentage reductions in air pollutants were observed during the change from normal days to MCO 1 (24% to 64%), while during MCO 1 to MCO 2, the concentrations were slightly increased during the changes of the lockdown phase, except for SO2 and NO2 over PJS. In KJ, most of the air pollutants decreased from MCO 1 to MCO 3 except for CO. However, the percentage reduction and increments of the gas pollutants were not consistent during the different phases of lockdown, and this effect was due to the sensor location—only 20 m from the main highway (vehicle emissions). The patterns of air pollutant concentrations over the KD site were similar to the PJS site; however, the percentage reduction and increases of PM2.5, O3, SO2 and CO were not consistent. We believe that local burning was the main contribution to these unstable patterns during the lockdown period. The cause of these different changes in concentrations may be due to the relaxation phases during the lockdown at each station, where most of the common activities, such as commuting and industrial activities changed in frequency from the MCO, CMCO and RMCO. Wind direction also affected the concentrations, for example, during the CMCO and RMCO, most of the pollutants were blowing in from the Southeast region, which mostly consists of a city center and industrial areas. There was a weak correlation between air pollutants and the temperature and relative humidity at all stations. Health risk assessment analysis showed that non-carcinogenic risk health quotient (HQ) values for the pollutants at all stations were less than 1, suggesting unlikely non-carcinogenic effects, except for SO2 (HQ > 1) in KJ. The air quality information showed that reductions in air pollutants can be achieved if traffic and industry emissions are strictly controlled.

PM2.5 levels strongly associate with multiple sclerosis prevalence in the Province of Padua, Veneto Region, North-East Italy

2018 ◽  
Vol 25 (13) ◽  
pp. 1719-1727 ◽  
Author(s):  
Fabio Tateo ◽  
Francesca Grassivaro ◽  
Mario Ermani ◽  
Marco Puthenparampil ◽  
Paolo Gallo
Keyword(s):  
Multiple Sclerosis ◽  
Urban Areas ◽  
Air Pollutants ◽  
Geographical Area ◽  
Average Concentration ◽  
Strongly Correlated ◽  
Annual Average ◽  
Pm 2.5 ◽  
North East ◽  
Annual Average Concentration

Background: Incidence and prevalence trends of multiple sclerosis (MS) in the Province of Padua, North-East Italy, suggest that environmental factors may be associated with increased MS risk. Objective: To investigate the association of PM2.5 with MS prevalence in one of the most polluted geographical area of Italy. Methods: In total, 1435 Italian MS patients residing in the Province of Padua were enrolled. The province surface was classified into urban areas, isolated villages, industrialized places, and countryside. Satellite-derived dust-free and sea salt-free PM2.5 concentrations (annual average 1998–2015, μg/m3) allowed the identification of 18 classes of territorial sections with statistically evaluable numbers of inhabitants. Possible correlations between residential locality types, PM2.5 concentrations, and MS prevalence were investigated. Results: MS prevalence was significantly ( p < 0.0001) higher in urban areas (ranging from 219 in Padua City to 169/100,000 in other urban areas) compared to isolated villages (116/100,000) or rural domains (109/100,000) and strongly correlated with the annual average concentration of PM2.5 ( r = 0.81, p < 0.001). Regression analysis further associated MS cases with PM.2.5 average concentration ( β = 0.11, p < 0.001). Conclusion: In the Province of Padua, MS prevalence is strongly associated with PM2.5 exposure suggesting that air pollutants may be one of the possible environmental risk factors for MS.

scholarly journals Long-term effects of fine particulate matter exposure on the progression of arterial stiffness

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Dianqin Sun ◽  
Yue Liu ◽  
Jie Zhang ◽  
Jia Liu ◽  
Zhiyuan Wu ◽  
...  
Keyword(s):  
Arterial Stiffness ◽  
Health Management ◽  
Ankle Brachial Index ◽  
Population Data ◽  
Average Concentration ◽  
Air Pollutant ◽  
Stiffness Index ◽  
Long Term Effects ◽  
Annual Average

Abstract Background Prior studies have investigated the association of PM2.5 exposure with arterial stiffness measured by ankle-brachial index (ABI) and brachial-ankle pulse wave velocity (baPWV), of which conclusions are inconsistent. Moreover, limited evidence is available on the contributory role of PM2.5 exposure on the arterial stiffness index. Methods We used the population data from the Beijing Health Management Cohort and conducted a longitudinal analysis. The annual average concentration of PM2.5 for 35 air pollutant monitoring sites in Beijing from 2014 to 2018 was used to estimate individual exposure by different interpolation methods. Multivariate logistic regression and linear regression were conducted to assess the association of annual average PM2.5 concentration with the incidence of higher baPWV, the progression of ABI, and baPWV, respectively. Results The association between PM2.5 exposure and incidence of higher baPWV was not significant (OR = 1.11, 95% CI: 0.82–1.50, P = 0.497). There was − 0.16% (95% CI: − 0.43-0.11%) decrease in ABI annually and 1.04% (95% CI: 0.72–1.37%) increase in baPWV annually with each increment of 10 μg/m3 average PM2.5 concentration. Conclusions Long-term exposure to PM2.5 was associated with the progression of arterial stiffness in Beijing. This study suggests that improvement of air quality may help to prevent arterial stiffness.

scholarly journals Heavy Metal Content in Water of Resko Lake (North-West Poland)

2013 ◽  
Vol 13 ◽  
pp. 279-287 ◽  
Author(s):  
Piotr Daniszewski ◽  
Ryszard Konieczny
Keyword(s):  
Surface Water ◽  
Water Samples ◽  
Metal Content ◽  
Research Work ◽  
Average Concentration ◽  
Permissible Limit ◽  
Toxic Heavy Metals ◽  
Annual Average ◽  
North West ◽  
Annual Average Concentration

The present research work deals with the quantification of toxic heavy metals in the water samples collected from Lake of Resko (North-West Poland). While the annual average concentration of Cadmium was calculated as 0.34 ppm in 2008 of the year and 0.28 ppm in 2009 of the year. The values obtained were found to be below the permissible limit of 2.0 ppm set for inland surface water. While the annual average concentration of Chromium was calculated as 1,75 ppm in 2008 of the year and 1.97 ppm in 2009 of the year. Which was very much above the permissible limit of 0.1 ppm set for inland surface water. The observed annual average concentration of Copper in the water was 0.05 ppm in 2008 of the year and 0.06 ppm in 2009 of the year, which was below the permissible limit of 3.0 ppm set for inland surface water. While the annual average concentration of Mercury was calculated as 0.03 ppm in 2008 of the year and 0.04 ppm in 2009 of the year, which was very much above the maximum limit of 0.01 ppm set for inland surface water. The annual average concentration of Nickel in the water samples was observed to be 2.07 ppm in 2008 of the year and 2.09 ppm in 2009 of the year, which is close to the limit of 3.0 ppm set for inland surface water. The annual average concentration of Pb in the water samples was observed to be 0.07 ppm in 2008 of the year and 0.05 ppm in 2009 of the year, which is above the permissible limit of 0.1 ppm set for inland surface water. The results of the present investigation indicate that the annual average concentration of Zn in water samples was 3.02 ppm in 2008 of the year and 2.74 ppm in 2009 of the year, which is above the permissible limit of 5.0 ppm set for inland surface water.

scholarly journals Quantifying the Health Benefits of Urban Climate Mitigation Actions: Current State of the Epidemiological Evidence and Application in Health Impact Assessments

2021 ◽  
Vol 3 ◽  
Author(s):  
Maria D. Castillo ◽  
Susan C. Anenberg ◽  
Zoe A. Chafe ◽  
Rachel Huxley ◽  
Lauren S. Johnson ◽  
...  
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Health Benefits ◽  
Health Impact ◽  
Epidemiological Studies ◽  
Health Improvement ◽  
Current State ◽  
The World ◽  
Climate Action ◽  
Health Impact Assessments

While ambitious carbon reduction policies are needed to avoid dangerous levels of climate change, the costs of these policies can be balanced by wide ranging health benefits for local communities. Cities, responsible for ~70% of the world's greenhouse gas (GHG) emissions and home to a growing majority of the world's population, offer enormous opportunities for both climate action and health improvement. We aim to review the current state of knowledge on key pathways leading from carbon mitigation to human health benefits, and to evaluate our current ability to quantify health benefits for cities around the world. For example, because GHGs and air pollutants are both released during fuel combustion, reducing fuel burning can reduce both GHGs and air pollutants, leading to direct health benefits. Air quality improvements may be particularly important for city-scale climate action planning because the benefits occur locally and relatively immediately, compared with the global and long-term (typically, decades to centuries) benefits for the climate system. In addition to improved air quality, actions that promote active transport in cities via improved cycling and pedestrian infrastructure can reap large cardiovascular health benefits via increased physical activity. Exposure to green space has been associated with beneficial health outcomes in a growing number of epidemiological studies and meta-analyses conducted around the world. Finally, noise is an underappreciated environmental risk factor in cities which can be addressed through actions to reduce motor vehicle traffic and other noise sources. All of these environmental health pathways are supported by well-conducted epidemiological studies in multiple locales, providing quantitative exposure–response data that can be used as inputs to health impact assessments (HIAs). However, most epidemiologic evidence derives from studies in high-income countries. It is unclear to what extent such evidence is directly transferable for policies in low- and middle-income countries (LMICs). This gap calls for a future focus on building the evidence based in LMIC cities. Finally, the literature suggests that policies are likely to be most effective when they are developed by multidisciplinary teams that include policy makers, researchers, and representatives from affected communities.

scholarly journals Mobile monitoring of urban air quality at high spatial resolution by low-cost sensors: Impacts of COVID-19 pandemic lockdown

2020 ◽  
Author(s):  
Shibao Wang ◽  
Yun Ma ◽  
Zhongrui Wang ◽  
Lei Wang ◽  
Xuguang Chi ◽  
...  
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Low Cost ◽  
Air Pollutant ◽  
Mitigation Strategies ◽  
Traffic Emissions ◽  
Urban Air ◽  
Industrial Emissions ◽  
Gis Technology ◽  
Mobile Monitoring

Abstract. The development of low-cost sensors and novel calibration algorithms provides new hints to complement conventional ground-based observation sites to evaluate the spatial and temporal distribution of pollutants on hyper-local scales (tens of meters). Here we use sensors deployed on a taxi fleet to explore the air quality in the road network of Nanjing over the course of a year (Oct. 2019–Sep. 2020). Based on GIS technology, we develop a grid analysis method to obtain 50 m resolution maps of major air pollutants (CO, NO2, and O3). Through hotspots identification analysis, we find three main sources of air pollutants including traffic, industrial emissions, and cooking fumes. We find that CO and NO2 concentrations show a pattern: highways > arterial roads > secondary roads > branch roads > residential streets, reflecting traffic volume. While the O3 concentrations in these five road types are in opposite order due to the titration effect of NOx. Combined the mobile measurements and the stationary station data, we diagnose that the contribution of traffic-related emissions to CO and NO2 are 42.6 % and 26.3 %, respectively. Compared to the pre-COVID period, the concentrations of CO and NO2 during COVID-lockdown period decreased for 44.9 % and 47.1 %, respectively, and the contribution of traffic-related emissions to them both decreased by more than 50 %. With the end of the COVID-lockdown period, traffic emissions and air pollutant concentrations rebounded substantially, indicating that traffic emissions have a crucial impact on the variation of air pollutants levels in urban regions. This research demonstrates the sense power of mobile monitoring for urban air pollution, which provides detailed information for source attribution, accurate traceability, and potential mitigation strategies at urban micro-scale.

Stand-alone low-cost sensor network in the inner city of Munich for modeling urban air pollutants

2021 ◽  
Author(s):  
Adrian Wenzel ◽  
Jia Chen ◽  
Florian Dietrich ◽  
Sebastian T. Thekkekara ◽  
Daniel Zollitsch ◽  
...  
Keyword(s):  
Air Quality ◽  
High Resolution ◽  
Sensor Network ◽  
Air Pollutants ◽  
Low Cost ◽  
Air Pollutant ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Small Scale ◽  
Urban Air

&lt;p&gt;Modeling urban air pollutants is a challenging task not only due to the complicated, small-scale topography but also due to the complex chemical processes within the chemical regime of a city. Nitrogen oxides (NOx), particulate matter (PM) and other tracer gases, e.g. formaldehyde, hold information about which chemical regime is present in a city. As we are going to test and apply chemical models for urban pollution &amp;#8211; especially with respect to spatial and temporally variability &amp;#8211; measurement data with high spatial and temporal resolution are critical.&lt;/p&gt;&lt;p&gt;Since governmental monitoring stations of air pollutants such as PM, NOx, ozone (O&lt;sub&gt;3&lt;/sub&gt;) or carbon monoxide (CO) are large and costly, they are usually only sparsely distributed throughout a city. Hence, the official monitoring sites are not sufficient to investigate whether small-scale variability and its integrated effects are captured well by models. Smart networks consisting of small low-cost air pollutant sensors have the ability to provide the required grid density and are therefore the tool of choice when it comes to setting up or validating urban modeling frameworks. Such sensor networks have been established and run by several groups, achieving spatial and temporal high-resolution concentration maps [1, 2].&lt;/p&gt;&lt;p&gt;After having conducted a measurement campaign in 2016 to create a high-resolution NO&lt;sub&gt;2&lt;/sub&gt; concentration map for Munich [3], we are currently setting up a low-cost sensor network to measure NOx, PM, O&lt;sub&gt;3&lt;/sub&gt; and CO concentrations as well as meteorological parameters [4]. The sensors are stand-alone, so that they do not demand mains supply, which gives us a high flexibility in their deployment. Validating air quality models not only requires dense but also high-accuracy measurements. Therefore, we will calibrate our sensor nodes on a weekly basis using a mobile reference instrument and apply the gathered sensor data to a Machine Learning model of the sensor nodes. This will help minimize the often occurring drawbacks of low-cost sensors such as sensor drift, environmental influences and sensor cross sensitivities.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;[1] Bigi, A., Mueller, M., Grange, S. K., Ghermandi, G., and Hueglin, C.: Performance of NO, NO2 low cost sensors and three calibration approaches within a real world application, Atmos. Meas. Tech., 11, 3717&amp;#8211;3735, https://doi.org/10.5194/amt-11-3717-2018, 2018&lt;/p&gt;&lt;p&gt;[2] Kim, J., Shusterman, A. A., Lieschke, K. J., Newman, C., and Cohen, R. C.: The BErkeley Atmospheric CO2 Observation Network: field calibration and evaluation of low-cost air quality sensors, Atmos. Meas. Tech., 11, 1937&amp;#8211;1946, https://doi.org/10.5194/amt-11-1937-2018, 2018&lt;/p&gt;&lt;p&gt;[3] Zhu, Y., Chen, J., Bi, X., Kuhlmann, G., Chan, K. L., Dietrich, F., Brunner, D., Ye, S., and Wenig, M.: Spatial and temporal representativeness of point measurements for nitrogen dioxide pollution levels in cities, Atmos. Chem. Phys., 20, 13241&amp;#8211;13251, https://doi.org/10.5194/acp-20-13241-2020, 2020&lt;/p&gt;&lt;p&gt;[4] Zollitsch, D., Chen, J., Dietrich, F., Voggenreiter, B., Setili, L., and Wenig, M.: Low-Cost Air Quality Sensor Network in Munich, EGU General Assembly 2020, Online, 4&amp;#8211;8 May 2020, EGU2020-19276, https://doi.org/10.5194/egusphere-egu2020-19276, 2020&lt;/p&gt;

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