PM10 Pollution Research Articles

Characteristics of fuelwood perturbation and effects on carbon monoxide and particulate pollutants emission from cookstoves in Nigeria

Water Boiling Test (WBT) was carried out to quantify carbon monoxide (CO) and particulate matter (PM2.5) emissions. Typical rural kitchen environment was modeled using the Laboratory Emission Monitoring System (LEMS), and a WBT tailored tool for Controlled Cooking Test (CCT). Five cookstoves were subjected to the WBT test and CO data generated were represented as CO-unvented natural draft (COUN), CO-natural draft with chimney (CONC), CO-three stone household cookstove (COTH), CO-domestic cookstove (CODS) and CO-general cookstove (COGE). Similarly, data generated for PM were represented as PM-unvented natural draft (PMUN), PM-natural draft with chimney (PMNC), PM-three stone household cookstove (PMTH), PM-domestic cookstove (PMDS) and PM-general cookstove (PMGE). Results obtained showed lower emissions for COGE (−0.16 g/MJ) and CODS (0.1 g/MJ), followed by CONC (12.81 g/MJ) and COUN (13.61 g/MJ), while COTH had the highest value of 20.21 g/MJ. Simialry, the results obtained for PM followed the CO emission trend as PMTH had the highest gaseous pollutant (729.15 mg/MJ), followed by PMNC (467.65 mg/MJ) and PMUN (406.63 mg/MJ). Cookstove data was further subjected to Fourier transform (MATLAB 12th Ed.) to examine the magnitude of the spectral as resultant of perturbation. The result was validated with a 2-tailed t-test and suggests that CO and PM pollutants are time dependent for all cookstoves. Improvement in design and ventilation, particularly for cookstoves, are crucial factors for creating a more beneficial health outcomes for those in the rural and peri-urban areas of low-income countries. The outcome of this study is important for policy makers in the area of improving the design and construction of cookstoves. It will also enable reforms in the architecture of the rural household kitchen for improved health and well-being in line with Sustainable Development Goals (SDGs) 3 and 7.

Dispersion of PM and VOC pollutants from open burning of municipal solid wastes on host communities: emission inventory estimation and dispersion modelling study

This study focused on the dispersion of PM and VOC pollutants from the open burning of solid waste on the Sokoto-Aiyekale dump site in Ilorin, Nigeria, owing to the scarcity of information on pollutants' impact on onsite workers and the population surrounding the dump site.

Chemical composition, levels, and I/O ratios of PM10 and PM2.5 in the laboratory near the copper smelter in Bor, Serbia

Numerous studies have reported that particulate matter and its specific chemical constituents were linked to the incidence of respiratory diseases and mortality and lung function. The main aim of this study is to determine metallic content in PM10 and PM2.5 samples taken simultaneously at several locations in the close vicinity of the copper smelter in Bor and the influence of outdoor PM pollution on indoor PM levels. The measurement campaign was conducted, during the non-heating season of 2020. PM samples were collected at all sampling sites with low-volume samplers (Sven/Leckel LVS3) on quartz fiber filters (Whatman QMA, 47mm). All samples were analyzed by Inductively Coupled Plasma Mass Spectrometry (ICP MS). In this way, the mass concentrations of 4 priority elements (As, Cd, Pb, and Ni) in PM samples were identified and quantified. It has been determined that average indoor PM levels in the laboratory were higher than outdoors. A strong correlation was found between PM10 and PM2.5 particle levels inside the laboratory and in the outdoor air. Also, a very strong correlation was found between the levels of Pb, Ni, As, and Cd determined in PM10 and PM2.5 samples inside the laboratory and in the outdoor air. This confirms that mentioned elements originate from the same sources located in the copper smelter complex.

The health risks of citizens due to PM2.5 exposure in Ha Noi, Vietnam

A cancerous and non-cancerous risk assessment for PM2.5 exposure is essential, especially in developing children. PM2.5 was determined in concentrations in the Hanoi area both inside and outside the house according to 2019 data before the Covid epidemic, and the health impact assessment was performed for each subject according to the US. Research has also shown that the distribution of indoor PM2.5 concentration data in Hanoi has been determined according to the Weibull with a shape parameter is 8,695 and a scale parameter is 3,695. The average daily indoor PM2.5 concentration at four houses was higher than 20 μg/m3 higher than the warning of WHO guidelines. However, the average concentration on some days exceeded the threshold of Vietnam’s standard. Meanwhile at K3, due to its location in the old town, the PM2.5 concentration is quite stable during the day and at a higher level than in other locations. Therefore, the effect of PM pollution in city houses is necessary for concern, monitoring and solving. This problem is not only present in Vietnam, this is the same in other countries such as some other research. So need to have guidelines when building new houses and inform citizens.

Short-term effects of ambient particulate matter (PM1, PM2.5 and PM10) on influenza-like illness in Guangzhou, China.

Particulate matter (PM) has been linked to respiratory infections in a growing body of evidence. Studies on the relationship between ILI (influenza-like illness) and PM1 (particulate matter with aerodynamic diameter ≤1μm) are, however, scarce. The purpose of this study was to investigate the effects of PM on ILI in Guangzhou, China. Daily ILI cases, air pollution records (PM1, PM2.5, PM10 and gaseous pollutants), and metrological data between 2014 and 2019 were gathered from Guangzhou, China. To estimate the risk of ILI linked with exposure to PM pollutants, a quasi-Poisson regression was used. Additionally, subgroup analyses stratified by gender, age and season were carried out. For each 10μg/m3 increase of PM1 and PM2.5 over the past two days (lag01), and PM10 over the past three days (lag02), the relative risks (RR) of ILI were 1.079 (95% confidence interval [CI]: 1.050, 1.109), 1.044 (95% CI: 1.027, 1.062) and 1.046 (95% CI: 1.032, 1.059), respectively. The estimated risks for men and women were substantially similar. The effects of PM pollutants between male and female were basically equivalent. People aged 15-24 years old were more susceptive to PM pollutants. It implies that PM1, PM2.5 and PM10 are all risk factors for ILI, the health impacts of PM pollutants vary by particle size. Reducing the concentration of PM1 needs to be considered when generating a strategy to prevent ILI.

Ambient air pollution monitoring and health studies using low-cost Internet-of-things (IoT) monitor within KNUST Community

Urban environments with high industrialization are infested with hazardous chemicals and airborne pollutants. These pollutants CO, O3, SO2, NO2, and PM can have devastating effects on human health, causing both acute and chronic diseases such as respiratory infections, lung cancer, and heart disease. Air pollution monitoring is vital to warn citizens of the health risks associated with exposure to high concentrations of these criteria pollutants. This study designed a low-cost IoT monitor to measure concentration levels of criteria pollutants emitted from transportation sources within Kwame Nkrumah University of Science and Technology environs. Three monitoring sites, KNUST Tech junction, Ayeduase gate junction and KNUST campus junction, were identified as the locations within the proximity of the university for the deployment of the monitor. Hourly and mean daily CO, NO2, O3 and SO2 concentrations at each of the three sites were measured for a week using the IoT monitor, when students were in school and when students were on vacation. The average daily CO, NO2 and O3 concentrations measured at the selected locations when school was in session and during vacation were presented on histogram. The mean weekly concentrations of CO, NO2 and O3 were also estimated as 13.2ppm, 0.277ppm and 0.106ppb respectively at KNUST Tech junction; 10.1ppm, 0.254ppm and 0.110ppb respectively at Ayeduase gate junction; and 8.0ppm, 0.415ppm and 0.100ppb respectively at the KNUST campus junction when school was in session. The results show that the concentrations of all the pollutants were higher and exceeded the EPA standards except for CO at KNUST Campus junction monitoring site. These high levels of emissions are an indication of a health concern for the students at the university and university authorities can device means of curbing it.

Small wood pellet boiler 3-D CFD study for improved flue gas emissions employing flue gas recirculation and air staging

Wood biomass fuels have become increasingly important in terms of achieving future sustainability targets regarding RES, especially to reduce GHG and the use of domestic energy sources, on other hand with these fuels the pulutant air emissions have to be addressed with great attention. Nowadays, air staging and flue gas recirculation are often used as a primary emission reduction measure in large scale biomass fuel fired boilers, but their combined application in small scale commercial wood pellet boilers is not so common. The implementation of this approach to the small scale burning devices can enable further development of small boilers, in order to achieve a more complete combustion and reduction of pollutant emissions, especially CO, NOx, and PM. In the presented work a comprehensive numerical and experimental study on the combustion parameters is performed, in order to determine the overall combustion properties. A commercial 32 kW small-scale hot water wood pellet boiler was modified numerically from the operating point of view. The findings of this study, like different combinations of recirculated flue gases and secondary air amount, can serve as useful guidelines for the new innovative design and optimisation of the air and recirculated flue gas injection process parameters. The scenarios of this study, where recirculated flue gas was added to the secondary air, were beneficial. Optimally, a CO reduction of 63% was achieved by adding 30% of recirculated flue gas to the secondary air stream, and a 22% reduction was achieved for the NO emissions.

Retracted: Exposure to Ambient Air Pollutant PM10 in the Second Trimester of Pregnancy Is Associated with Preterm Birth: A Birth-Based Health Information Cohort Study.

[This retracts the article DOI: 10.1155/2022/1008538.].

Study on the Trend of Atmospheric Air Quality Changes, Pollution Characteristics and Causes in the Chengdu-Chongqing Region in China

Based on the air pollutant spatial and temporal data of 16 major cities in the Chengdu-Chongqing region from 2015-2021, using the analysis methods of the spatial autocorrelation model and the characteristic radar chart, this paper analyzes the spatial and temporal characteristics of air pollution in the Chengdu-Chongqing region and the main sources and causes of air pollutants in different cities. The research results show: (1) temporally, the overall air quality in the Chengdu-Chongqing region shows an improving trend from 2015 to 2021, but the pollution is more serious in winter, and the air pollutants fine particulate matter(PM2.5) and sulfur dioxide(SO2) pollution are prominent. (2) Spatially, the spatial distribution pattern of pollutants PM2.5, inhalable particles (PM10), nitrogen dioxide(NO2) and SO2 in Chengdu-Chongqing region shows the distribution characteristics of high in the west and low in the east, while pollutant carbon monoxide(CO) shows the characteristics of high in the east and low in the west. (3) In 2021, the air quality index (AQI index) of cities in Chengdu-Chongqing region show the spatial clustering characteristics of "high - high" and "low - low". (4) The urban air pollution in Chengdu-Chongqing region mainly includes four types: secondary-biased, coal-fired, motor vehicle-biased and dust-biased, among which, coal-fired is the main type of air pollution in Chengdu-Chongqing region.The findings of the study can provide decision support for targeted treatment of air pollution and high-quality economic and social development in the Chengdu-Chongqing region.

Comparison of Air Pollutants and Air Quality Index using Spatio-Temporal Variation in Chennai City, Tamil Nadu

In recent years, air pollution in Chennai city in India causes some health effects. This study examines the spatial-temporal characteristics of ambient air quality in five stations Adyar, Anna Nagar, Kilpauk, Nungambakkam and Thiyagaraya Nagar from 2017 to 2022. The surface level aerosol pollutants like particulate matters (PM2.5 & PM10) and gaseous pollutants Sulfur dioxide (SO2) & Nitrogen dioxide (NO2) were obtained from Tamilnadu Pollution Control Board (TPCB) for five years which includes pre-COVID, during and Post- COVID - period. The results showed that fine particulate matter (PM2.5) and coarse particulate matter (PM10), decreased by 19.49% and 31.91% respectively and gaseous pollutant SO2 and NO2 slightly increased by 7.84% and 1.2 % respectively during 2021 as compared with 2017.The particulate matter (PM2.5 & PM10) level exceeded the National Ambient Air Quality Standards (NAAQS) as well as the WHO recommended Air Quality Guidelines during 2017-2019(Pre-COVID) and low during 2020-2021(During COVID and Post-COVID). The average Air Quality Index (AQI), calculated from the date decreased from 120(2018-2019) to 93(2020-2021) in Chennai city. The AQI and PM2.5/PM10 showed the highest pollution level in winter and lower in summer. PM10 was the primary pollutant, followed by NO2, PM2.5 & SO2 with spatial and temporal variations. The proportion of pollutants PM2.5 and PM10 decreased but increased for SO2 and NO2. This study offers useful data and resources for further research on Chennai's air quality.

Multi-layer long short-term memory (LSTM) prediction model on air pollution for Konya province

One of the main problems of the developing and changing world is air pollution. In addition to human causes such as population growth, increase in the number of vehicles producing exhaust emissions in line with the population, development of industry, natural causes such as forest fires, volcano eruptions and dust storms also play a role in increasing air pollution. Air pollution has become a bigger problem that reduces the quality of life of living beings and causes various lung and heart diseases due to reasons such as the growing proximity of settlements to industrial zones due to population growth, the increase in the number of individual vehicles, and zoning works carried out by ignoring air quality. Both international organizations and local authorities take various measures to control and prevent air pollution. In Turkey, necessary legal arrangements have been made within the scope of these measures and air quality monitoring stations have been established. The task of these stations is to measure pollutants such as PM10, CO, SO2 together with meteorological data such as air temperature, humidity, wind speed and direction. In this study, a prediction model for the future concentrations of PM10, CO and SO2 pollutants using the measurement data from three different air quality monitoring stations in Konya between January 2020 and January 2021 was realized with a multi-layer Long Short Term Memory (LSTM) artificial neural network. The Root Mean Square Deviation (RMSE) and Mean Absolute Percentage Error (MAPE) methods was used to calculate the performance of the study. As a result of the study, it is observed that the multi-layer LSTM architecture is more successful than the single-layer architecture.

A Bidirectional Long Short-Term Neural Network Model to Predict Air Pollutant Concentrations: A Case Study of Tehran, Iran

Air pollution is a major challenge of new civilized world, which has intensified as a result of industrialization, urbanization expansion, and rapid traffic growth and increasing anthropogenic activities. Air pollutants can cause serious toxicological impacts on human health and the environment. Therefore, knowledge of pollutant concentrations can be used as key information in pollution control programs and policies. The main aim of this research was to provide a new hybrid model for short-term air quality prediction. Air pollution is the presence of pollutants in the air such as sulphur dioxide (SO2), particle substances (PM), nitrogen oxides (NOX), and ozone (O3). Sulfur dioxide (SO2), particulate matter with particle size less than 10 microns (PM10), particulate matter (PM2.5), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) in the air all have an impact on the Air Quality Index (AQI). The AQI's variation is extremely nonlinear and complex. One of the most desired features of an AQI prediction model is the ability to predict the AQI level that is dangerous to the population. To increase forecast accuracy, an air quality prediction model based on deep recurrent neural networks and the BiLSTM model is created in this research. AQIs (O3, PM2.5, PM10, NO2, SO2, CO) prediction has been examined among several air pollutants. The data on O3, PM2.5, PM10, NO2, SO2, and CO concentrations are average daily reports from the Tarbiat Modares station in Tehran's 6th district, collected between March 2018 and June 2019. The station is located at 35.71751 latitude and 51.385909 longitudes. The proposed model was compared with Shallow Learning and MLP neural networks which had better and more reliable results over the aforementioned neural network in predicting the concentration of pollutants. The regression coefficient obtained to predict the concentrations of pollutants O3, PM2.5, PM10, NO2 and SO2 was 0.87, 0.62, 0.84, 0.67, 0.75 and 0.72, respectively. The results showed that the Bi-LSTM deep neural network model is a reliable method to predict hourly air pollutant concentrations.

THE IMPACT OF AIR QUALITY AND METEOROLOGICAL CONDITIONS INCLUDING VISIBILITY ON TOURISM: THE CASE OF ZAKOPANE (POLAND)

The aim of the study was to investigate the impact of air pollution and meteorological conditions on visibility in Zakopane, the most popular tourist destination in southern Poland. To achieve this objective, correlation analysis, multiple linear regression analysis and random forests were used. In addition, an analysis was performed of the occurrence of episodes of elevated pollutant concentrations and basic statistical characteristics of visibility, meteorological conditions and air pollution. Meteorological parameters (air temperature, relative humidity, total precipitation, wind speed, atmospheric pressure and visibility) and concentrations of air pollutants; particulate matter (PM₁₀) and gaseous pollutants (SO₂, NO₂) were recorded from 2010 to 2019. The data came from a monitoring station located in Zakopane-MpZakopaSien. It was found that high concentrations of air pollutants SO₂ and PM₁₀, along with relative humidity (RH), were the most important parameters affecting visibility limitation in Zakopane. Concentrations and indirectly also visibility were largely influenced by increased automobile traffic due to tourist activity, as well as emissions from apartment/building heating (combustion of various fuels). Understanding the relationship between air pollutant concentrations, meteorological conditions and visibility is a prerequisite and the basis for the scientific formulation of air pollution prevention and control policies in places where monitoring is particularly important.

Tracking sources of PM10 emissions and deposition in the industrial city of Ostrava, Czech Republic: A carbonaceous δ13C-based approach

The sources of airborne particulate matter (PM10) emissions in Ostrava, Czech Republic, were investigated. Emphasis was placed on their organic carbon (OC) and elemental carbon (EC) contents, and their carbon stable isotope composition, δ13C. Emission sources were identified using OC-δ13C and concentration values. To track the extent of long-term deposition, these sources were also identified using the black carbon (BC) δ13C values of soil samples. At all sampling sites, wind flow is predominantly (65–80%) bidirectional in either SW-NE or NE-SW trajectories. Source apportionment along these dominant airflow trajectories was calculated from an isotopic 13C mass balance, and according to differences in the OC content and δ13C values of PM10. Determined emission sources are: (i) combustion of Silesian hard coal (δ13C = −24.5‰); (ii) local Ostrava coal combustion (δ13C = −25.5 to −26‰), automotive emissions (δ13C = −26.5‰), and biogenic particles (δ13C = −28 to −28.5‰). Winter emissions (mean OC concentrations from 12 to 25 μg m−3) originated mostly from coal combustion (80%) in domestic and industrial point sources. Differences were ascribed to automotive emissions. Ostrava is located near the Czech-Polish border, transboundary emissions are transported under a southbound wind flow that transported from 40 to 80% of the collected PM10. Summer emissions were lower (mean OC concentration from 6 to 8 μg m−3). Automotive emissions accounted for up to 40%, whilst biogenic production accounted for 60%. Absence of 13C isotope data of secondary OC (SOC 1.2–1.5 μg m−3) increases uncertainty in our source apportionment in summer; when SOC could comprise >20% of total OC. Contribution of SOC to the winter-measured OC is much lower (5–10%), and has no significant effect on mass balances. The upper soil layer analyses revealed long-term deposition of the same emissions sources. We conclude that the stable δ13C isotope values of OC and EC are useful for discriminating against local sources of PM10 pollution in relatively small urban areas, containing discrete polluting sources. Such a simplified approach can be easily standardized and implemented to manage regulatory compliances in the increasingly commoditized carbon offset market.

Impacts of Air Pollution on the Respiratory System of Adults in Relation to Socioeconomic Status

Air pollution has a significant impact on respiratory health, yet comprehensive summaries of specific impacts are limited. This study reviews previous research done on this link, while connecting it to socioeconomic factors. Common air pollutants, such as particulate matter, have a large impact on respiratory health and can exacerbate diseases and medical conditions, such as coughing, wheezing, chronic obstructive pulmonary disease, asthma, and cystic fibrosis. Specifically, PM10 pollutants can deposit in the upper airways, and PM2.5 can reach deep into the lungs, leading to many complications. The efficacy of measures taken to combat these issues are also discussed. In recent years, governments have introduced national climate policies and green-space designs in urban municipalities to reduce negative health outcomes. These measures accomplish this goal to a limited degree but require further investment and development. To outline how socioeconomic status impacts air pollution-related respiratory illnesses, our study discusses five studies investigating socioeconomic disparities in air pollution exposure in various parts of the world. Evidence shows that low-income people typically reside in areas with high air pollution because housing is more affordable. This provides an opportunity for new research in social determinants of health to better understand its connection with human health.

Clustering-Based Segmented Regression for Particulate Matter Sensor Calibration

Nowadays, sensor-based air pollution sensing systems are widely deployed for fine-grained pollution monitoring. In-field calibration plays an important role in maintaining sensory data quality. Determining the model structure is challenging using existing methods of variable global fitting models for in-field calibration. This is because the mechanism of interference factors is complex and there is often insufficient prior knowledge on a specific sensor type. Although Artificial-Neuron-Net-based (ANN-based) methods ignore the complex conditions above, they also have problems regarding generalization, interpretability, and calculation cost. In this paper, we propose a clustering-based segmented regression method for particulate matter (PM) sensor in-field calibration. Interference from relative humidity and temperature are taken into consideration in the particulate matter concentration calibration model. Samples for modeling are divided into clusters and each cluster has an individual multiple linear regression equation. The final calibrated result of one sample is calculated from the regression model of the cluster the sample belongs to. The proposed method is evaluated under in-field deployment and performs better than a global multiple regression method both on PM2.5 and PM10 pollutants with, respectively, at least 16% and 9% improvement ratio on RMSE error. In addition, the proposed method is insensitive to reduction of training data and increase in cluster number. Moreover, it may bear lighter calculation cost, less overfitting problems and better interpretability. It can improve the efficiency and performance of post-deployment sensor calibration.

Staggered-peak production is a mixed blessing in the control of particulate matter pollution.

Staggered-peak production (SP)-a measure to halt industrial production in the heating season-has been implemented in North China Plain to alleviate air pollution. We compared the variations of PM1 composition in Beijing during the SP period in the 2016 heating season (SPhs) with those in the normal production (NP) periods during the 2015 heating season (NPhs) and 2016 non-heating season (NPnhs) to investigate the effectiveness of SP. The PM1 mass concentration decreased from 70.0 ± 54.4 μg m-3 in NPhs to 53.0 ± 56.4 μg m-3 in SPhs, with prominent reductions in primary emissions. However, the fraction of nitrate during SPhs (20.2%) was roughly twice that during NPhs (12.7%) despite a large decrease of NOx, suggesting an efficient transformation of NOx to nitrate during the SP period. This is consistent with the increase of oxygenated organic aerosol (OOA), which almost doubled from NPhs (22.5%) to SPhs (43.0%) in the total organic aerosol (OA) fraction, highlighting efficient secondary formation during SP. The PM1 loading was similar between SPhs (53.0 ± 56.4 μg m-3) and NPnhs (50.7 ± 49.4 μg m-3), indicating a smaller difference in PM pollution between heating and non-heating seasons after the implementation of the SP measure. In addition, a machine learning technique was used to decouple the impact of meteorology on air pollutants. The deweathered results were comparable with the observed results, indicating that meteorological conditions did not have a large impact on the comparison results. Our study indicates that the SP policy is effective in reducing primary emissions but promotes the formation of secondary species.

Assessment of health risks for criteria air pollutants present in 11 non-attainment cities of Uttar Pradesh, India

Uttar Pradesh (UP), one of the most populous state of India is facing the highest mortality rate due to ambient air exposure. In this study, the Relative Risk (RR) and Excess Risk (ER) due to six criteria air pollutants namely PM10, PM2.5, SO2, NO2, O3, and CO have been estimated for 11 non-attainment cities (NAC) of this State. Results indicated that the estimated total ER was 2.62 times higher when calculated using WHO 2021 Air Quality standards (AQS) and compared with estimates using India’s CPCB 2009 AQS. Ghaziabad faces the highest ERs for both PM10 (6.20%) and PM2.5 (3.9%) as per WHO 2021 AQS. ER due to NO2 was observed highest for Kanpur. The ERs obtained for CO, SO2, and O3 were found negative mainly because the estimated pollutant concentration was observed less than AQS and therefore possess no health risks. The study concludes that the present AQS of India’s CPCB for air pollutants PM10, PM2.5, and NO2 are underestimating the excess health risks and need to be revised urgently for better action plans in saving lives.

Influence of extratropical cyclones on air quality in Beijing

Extratropical cyclones (ETCs) are one of the primary weather systems affecting China. However, atmospheric environmental effects generated under the control of ETCs are rarely reported. In this study, we investigate air quality in Beijing from the perspective of ETCs. The ETCs affecting Beijing from 2014 to 2021 are tracked using the 850 hPa relative vorticity cyclone tracking algorithm. We classify the identified ETCs by cyclogenesis region, which includes 228 northern cyclones (NCs) and 41 southern cyclones (SCs). Generally, NCs are more likely to cause air pollution. For 27.4% and 16.3% of the days under the control of NCs, PM2.5 and PM10 pollution occur, respectively. These are mainly associated with high northwest wind and enhanced sinking motion. SCs are conducive to good or moderate air quality. The PM2.5/PM10 pollution days account for 20.0%/5.0% of total days under the control of SCs, mainly due to enhanced rising motion and increased precipitation. Nevertheless, the influence of NCs/SCs on air quality shows significant seasonal differences. NCs negatively impact air quality in most seasons, except in summer. SCs tend to reduce the probability of air pollution in summer and autumn. Higher chances of air pollution associated with SCs occur in spring and winter. For a typical NC case that causes a dust storm, dust backflow occurs after the NC moves away, resulting in secondary pollution. A typical SC case generates different atmospheric environmental effects owing to the changes in its location. These results highlight the importance of ETCs for air quality in Beijing.

Public health in Global South: effect of environmental pollutant PM2.5 on the incidence and mortality of SARS-CoV-2 in Karachi, Lahore, and Islamabad.

Environmental pollution has undoubtedly been established as a planetary, intergenerational, and existential threat to global human health and safety. Environmental pollution is adversely affecting the world, mainly the countries where human health is not a priority aspect, and this has been exacerbated due to the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), and pandemic is known as "COVID pandemic". This study investigates the association of environmental pollutants, particulate matter (PM2.5), with SARS-CoV-2 daily cases and deaths in Karachi, Lahore, and Islamabad, Pakistan, presenting the perspectives from the Global South. The day-to-day PM2.5 levels were recorded from the metrological website, Real-Time Air Quality Index-AQI. The corresponding data on the COVID cases and deaths in Karachi, Lahore, and Islamabad were obtained from August 1, 2020, to September 30, 2021, from the Health Ministry and National Command Operations Centre Pakistan. The mean values for PM2.5 in Karachi were 110.4±46.2; in Lahore 174.0±83.2; and in Islamabad 107.1±40.0. The COVID-19 mean daily cases in Karachi were 538.9±446.6; Lahore 398.3±403.1; and Islamabad 212.2±187.6; and mean daily deaths in Karachi were 9.2±8.3; Lahore 9.3±9.7; and Islamabad 1.8±1.8. The results further depicted that the SARS-CoV-2 cases were 2.86 times higher in Karachi and 1.4 times higher in Lahore than in Islamabad. Similarly, the SARS-CoV-2 deaths were 3.6 and 2.8 times higher in Karachi and Lahore, respectively, compared to Islamabad. The findings claim that cases and deaths augmented significantly along with PM2.5 levels. These empirical estimates demonstrate an association between PM2.5 and SARS-CoV-2 daily cases and deaths in the cities of the Global South. These findings can contribute to policy-making decisions about addressing air pollutants and climate concerns in developing countries and create an urgency to develop a strategy for minimizing environmental pollution. This study can also steer the actions needed to address the environmental problems in developing countries to improve public health and safety.