Concentrations Of Various Pollutants Research Articles

Urban air pollution evaluation in downtown streets of a medium-sized Latin American city using AERMOD dispersion model.

The transport sector is considered the largest contributor of air pollutants in urban areas, mainly on-road vehicles, affecting the environment and human health. Bahía Blanca is a medium-sized Latin American city, with high levels of traffic in the downtown area during peak hours. In this regard, it is necessary to analyze air pollution using an air quality model considering that there are no air pollutant measurements in the central area. Furthermore, this type of study has not been carried out in the region and since the city is expected to grow, it is necessary to evaluate the current situation in order to make effective future decisions. In this sense, the AERMOD model (US-EPA version) and the RLINE source type were used in this work. This study analyzes the variations of pollutant concentrations coming from mobile sources in Bahía Blanca's downtown area, particularly carbon monoxide (CO) and nitrogen oxides (NOx) during the period Jul-2020 to Jun-2022. It is interesting to note the results show the maximum concentration values detected are not directly associated with maximum levels of vehicle flow or emission rates, which highlights the importance of meteorological parameters in the modeling. In addition, alternative scenarios are proposed and analyzed from a sustainable approach. Regarding the scenario analysis, it can be concluded that diesel vehicles have a large influence on NOx emissions. Moreover, restrictions as strict as those proposed for a Low Emission Zone would be less applicable in the city than alternative temporary measures that modify traffic at peak hours.

Long-term variations of air pollutants and public exposure in China during 2000–2020

Since 2000, China has faced severe air pollution challenges，prompting the initiation of comprehensive emission control measures post-2013. The subsequent implementation of these measures has led to remarkable enhancements in air quality. This study aims to enhance our understanding of the long-term trends in fine particulate matter (PM2.5) and gaseous pollutants of ozone (O3) and nitrogen dioxide (NO2) across China from 2000 to 2020. Utilizing the Community Multiscale Air Quality (CMAQ) model, we conducted a nationwide analysis of air quality, systematically quantifying model predictions against observations for pollutants. The CMAQ model effectively captured the trends of air pollutants, meeting recommended performance benchmarks. The findings reveal variations in pollutant concentrations, with initial increases in PM2.5 followed by a decline after 2013. The proportion of the population living in high PM2.5 concentrations (>75 μg/m3) decreased to <5 % after 2015. However, during the period from 2017 to 2020, around 40 % of the population continued to live in regions that did not meet the criteria for Chinese air quality standards (35 μg/m3). From 2000 to 2019, fewer than 20 % of the population met the WHO standard (100 μg/m3) for MDA8 O3. In 2000, 77 % of the population met the NO2 standard (<20 μg/m3), a figure that declined to 60 % between 2005 and 2014, nearly reaching 70 % in 2020. This study offers a comprehensive analysis of the changes in pollutants and public exposure in 2000–2020. It serves as a foundational resource for future efforts in air pollution control and health research.

Spatio-temporal evaluation of air pollution using ground-based and satellite data during COVID-19 in Ecuador

The concentration of gases in the atmosphere is a topic of growing concern due to its effects on health, ecosystems etc. Its monitoring is commonly carried out through ground stations which offer high precision and temporal resolution. However, in countries with few stations, such as Ecuador, these data fail to adequately describe the spatial variability of pollutant concentrations. Remote sensing data have great potential to solve this complication. This study evaluates the spatiotemporal distribution of nitrogen dioxide (NO2) and ozone (O3) concentrations in Quito and Cuenca, using data obtained from ground-based and Sentinel-5 Precursor mission sources during the years 2019 and 2020. Moreover, a Linear Regression Model (LRM) was employed to analyze the correlation between ground-based and satellite datasets, revealing positive associations for O3 (R2 = 0.83, RMSE = 0.18) and NO2 (R2 = 0.83, RMSE = 0.25) in Quito; and O3 (R2 = 0.74, RMSE = 0.23) and NO2, (R2 = 0.73, RMSE = 0.23) for Cuenca. The agreement between ground-based and satellite datasets was analyzed by employing the intra-class correlation coefficient (ICC), reflecting good agreement between them (ICC ≥0.57); and using Bland and Altman coefficients, which showed low bias and that more than 95% of the differences are within the limits of agreement. Furthermore, the study investigated the impact of COVID-19 pandemic-related restrictions, such as social distancing and isolation, on atmospheric conditions. This was categorized into three periods for 2019 and 2020: before (from January 1st to March 15th), during (from March 16th to May 17th), and after (from March 18th to December 31st). A 51% decrease in NO2 concentrations was recorded for Cuenca, while Quito experienced a 14.7% decrease. The tropospheric column decreased by 27.3% in Cuenca and 15.1% in Quito. O3 showed an increasing trend, with tropospheric concentrations rising by 0.42% and 0.11% for Cuenca and Quito respectively, while the concentration in Cuenca decreased by 14.4%. Quito experienced an increase of 10.5%. Finally, the reduction of chemical species in the atmosphere as a consequence of mobility restrictions is highlighted. This study compared satellite and ground station data for NO2 and O3 concentrations. Despite differing units preventing data validation, it verified the Sentinel-5P satellite's effectiveness in anomaly detection. Our research's value lies in its applicability to developing countries, which may lack extensive monitoring networks, demonstrating the potential use of satellite technology in urban planning.

Variation in urban snow quality indicated by three seasonal sampling surveys conducted in Luleå (Sweden) within a span of 27 years

Concentrations of total suspended solids (TSS), trace metals (Cu, Cd, Cr, Zn, Cd, Pb), Na and 16 US EPA priority PAHs in urban snow were studied in the City of Luleå in Northern Sweden. Snow was sampled at six central urban and suburban sites with various traffic intensities, in three sampling surveys (1994–95, 2002–03, 2020–21), repeated for three ages of the urban snow cover of 40, 80, and 120 days, respectively. The older data, from the 1994–95 and 2002–03 surveys, were obtained from the existing literature. The concentrations and mass loads of TSS and most trace metals studied (Zn, Cu, Pb, and Cd) varied with time. TSS, Zn, and Cu showed slightly higher concentrations and mass loads in the 2003 (TSS avg = 2300 μg/L, Zn avg = 620 μg/L and Cu avg = 250 μg/L) and 2021 (TSS avg = 1500 μg/L, Zn avg = 530 μg/L and Cu avg = 220 μg/L) sampling surveys, compared to the 1995 survey (TSS avg = 620 μg/L, Zn avg = 240 μg/L and Cu avg = 97 μg/L). However, no evident trend was observed between the 2003 and 2021 sampling surveys. The highest concentrations of Pb and Cd were observed in snow samples from the 1994–95 sampling survey (Pb max = 570 μg/L, Cd max = 4.6 μg/L). Results indicated higher concentrations of the pollutants studied in the city centre, compared to the residential suburbs, and in areas with heavier traffic, where concentrations of metals correlated well with traffic intensity. Fractionation analysis of trace metals indicated that Zn, Cu and Pb occurred mostly in the particulate-bound phase (>0.45 μm) containing the most of Zn, Cu, and Pb mass, at 80, 84 and 94% of the total, respectively. Over 50% of the dissolved phase of Zn and Cu was in the truly dissolved fraction (<3000 MWCO). Concentrations of PAHs also increased with traffic intensity, with pyrene being the most frequently detected PAH, likely because of the strength of sources and various physical processes influencing the snowbanks development and causing spatial and temporal variations in pollutant concentrations.

Characterization of summertime single aerosol particles in Chengdu (China): Interannual evolution and impact of COVID-19 lockdown

To investigate the interannual evolution of air pollution in summer and the impact of the COVID-19 lockdown on local pollution in Chengdu, China, single aerosol particles were continuously measured in three summer periods: the regular period in 2020 (RP2020); the regular period in 2022 (RP2022); and the lockdown period in 2022 (LP2022). It was found that, from RP2020 to RP2022, the mass concentrations of PM2.5, PM10, SO2 and NO2 decreased by 25.6 %, 24.7 %, 28.8 % and 38.5 %, respectively, while the concentration of O3 increased by 11.0 %. Affected by regional transport, there was no significant decrease in the concentrations of various pollutants during LP2022. All single aerosol particles could be classified into seven categories: vehicle emissions (VE), dust, biomass burning (BB), coal combustion (CC), K mixed with sulfate (KSO4), K mixed with nitrate (KNO3) and K mixed with sulfate and nitrate (KSN) particles. From RP2020 to RP2022, the contributions of BB and CC particles decreased by 12.1 % and 0.9 %, respectively, while VE and dust particles increased by 3.6 % and 2.5 %, respectively; and compared to RP2022, the contributions of VE, dust and CC particles in LP2022 decreased by 22.2 %, 11.0 % and 12.7 %, respectively. The high PM2.5 pollution events in RP2020 and RP2022 were mainly caused by combustion sources (BB and CC, 51.6 %) and VE (38.3 %) particles, respectively, while the pollution event in LP2022 was contributed by BB (27.0 %) and secondary inorganic (KSO4, KNO3 and KSN, 60.2 %) particles. The formation mechanisms of different pollution events were further validated by WRF-Chem results. Although the potential source areas of particles showed a shrinking trend from RP2020 to RP2022, regional transport still caused high PM2.5 pollution events during LP2022. Photochemical processes dominated the formation of KSO4 particles, while the KNO3 and KSN particles were mainly generated by liquid-phase reactions, and this effect increased year by year.

Health and economic benefits of heavy-duty diesel truck emission control policies in Beijing

PM2.5 emissions from heavy-duty diesel trucks (HDDTs) have a significant impact on air quality, human health, and climate change, and seriously threaten the UN Sustainable Development Goals. Globally, a series of emission control measures have been implemented to reduce pollution emissions from HDDTs. Current studies assessing the impact of these measures on air quality and human health have mainly used coarse-grained emission data as input to dispersion model, resulting in the inability to capture the spatiotemporal variability of pollutant concentrations and tending to increase the uncertainty of health impact assessment results. In this study, we quantified the impact of pollution control policies for HDDTs in Beijing on PM2.5 concentrations, human health, and economic losses by integrating policy scenario analysis, pollution dispersion simulation, public health impact and economic benefit assessment models, supported by high spatiotemporal resolution emission data from HDDTs. The results show that PM2.5 concentrations from HDDTs exhibit significant spatial aggregation characteristics, with the intensity of aggregation at night being about twice as high as that during the day. The emission hotspots are mainly concentrated in the sixth, fifth and fourth rings and major highways. Compared to the “business as usual” scenario in 2018, the current policy of updating the fuel standard to China VI and the emission standard to China 6 can reduce PM2.5 concentrations by 96.72%, thereby avoiding 612 premature deaths, which is equivalent to obtaining economic benefits of 1.65 billion CNY. This study further emphasizes the importance of high spatiotemporal resolution emission data during traffic dispersion modeling. The results can help improve the understanding of the effectiveness of emission reduction measures for HDDTs from a health benefit perspective.

Measurements of traffic-related air pollution at a U.S.–Mexico port of entry and its impacts on nearby community

Traffic-related air pollutants are especially prevalent in border cities with multiple ports of entry (POEs). Excessive emissions due to long delays of idling commercial and passenger vehicles at the POEs could exacerbate various types of respiratory health conditions for the POE users and workers. We conducted a 1-month in-traffic air monitoring campaign at a POE in El Paso, Texas using three continuous FEM instruments for three criteria pollutants (PM, O3, and NO2). Concurrent monitoring of PM in the nearby community was performed using a number of low-cost PM sensors. The 1-month average PM2.5 concentration at the POE was found below 11.6 µg/m3, while the 5-min average of in-traffic PM2.5 concentration could be as high as 450 µg/m3. The in-traffic PM2.5 concentrations were comparable to the data concurrently measured in a state-operated community monitoring site approximately 0.4 km away from the POE. Ozone (O3) concentrations remained low in the region. The in-traffic 1-h NO2 concentrations were below the 1-h NAAQS of 100 ppb, except for only two 5-min intervals. Our results suggest that traffic emissions do not result in elevated levels of pollutant concentrations at this POE compared to the concentration levels observed in the nearby community. In addition, the performance and accuracy of the low-cost sensors appear to be less reliable during our study, although the devices were capable of detecting the trends and variability in pollutant concentrations in real time.

City Wind Impact on Air Pollution Control for Urban Planning with Different Time-Scale Considerations: A Case Study in Chengdu, China

Economic development and fast growing urbanization in China have caused severe air pollution, with frequent pollution episodes endangering the health of inhabitants and disturbing social activities, and as an expanding metropolis, Chengdu has suffered ever since. The concentration variations of main air pollutants, such as PM10, PM2.5 and NO2, often show periodicity because of meteorological impact and anthropic activities, and display orientation discrepancies due to influences of wind speed (WS), frequency and pollutant sources. These features have complicated the mechanisms of pollution episodes and deepened the difficulty in pollution control evaluation. The WS has significant influences on the periodicity and orientation variations in pollutant concentrations, and quantifying the influence of which is of high significance and provides sustainable foundations for pollution alleviation strategies. Different time-scale cycles (i.e., Diurnal, weekly, seasonal and annual), along with the WS, wind frequency, wind and spatial orientations in urban areas, were analyzed in this paper. Results show that the periodicity of diurnal, seasonal and annual cycles is remarkable, and weekly cycle is obvious by adding the influence of the WS in 16 orientations. The WS has direct impacts on pollutants varying in the range of 1.5–2.5 m/s, and has a remarkable diffusion effect on pollutants once above 2.5 m/s. Over heavy pollution hours in diurnal, weekly, annual cycles and transitional seasons, the WS had more significant influences on pollutants, and whereas the wind frequency is not the main impact factor for orientation variations. For Chengdu, the northeast orientation is suitable to construct a wind panel with a remarkable diffusion effect on pollutants, while air pollutions in the northwest and southwest orientations were severe with the WS below 1.5 m/s, and pollution diffusion in the north-northwest orientation was the worst. This work can provide guidance and reference for urban planning optimization and air environment protection in cities with air quality control considerations impacted by city wind.

Spatiotemporal Assessment of Atmospheric Pollutants in Yancheng City, Eastern Coastal City of China

Atmospheric environmental pollution has become a critical issue in eastern coastal cities in China, so a broad understanding of its spatiotemporal characteristics is of importance to develop public policies. In this study, hourly data of ρ(PM2.5), ρ(PM10), ρ(NO2), ρ(SO2), ρ(O3) and φ(CO) of five different types of national air quality monitoring sites from 2016 to 2020 were analyzed, combined with the change of meteorological elements in the same period in Yancheng, which was a rapidly developed eastern coastal city in China. The results indicated that the pollutant concentrations except for ρ(O3) was low in summer and high in winter, decreasing year by year from 2016 to 2020. The proportion of moderately and heavily contaminated days in the whole year was decreasing from 80 days in 2016 to 52 days in 2020, and the days with good quality increased from 284 days in 2016 to 311 days in 2020. ρ(O3) was the highest in spring and the lowest in winter, increasing slightly year by year. The variation of ρ(PM2.5), ρ(PM10), ρ(NO2), ρ(SO2) and φ(CO) showed a double-peak type, reaching the peak value at 8:00–10:00 and 20:00–22:00, corresponding to the early and evening rush hours. ρ(PM2.5), ρ(PM10) and φ(CO) on the weekend were higher than on weekdays, while an insignificant difference of ρ(NO2), ρ(O3) and ρ(SO2) was found between weekdays and the weekend. Wind direction played a key role in the variation of pollutant concentration in the Yancheng urban area, and the correlation analysis indicated that ρ(PM2.5) and ρ(PM10) were highly correlated to wind direction. Temperature was positively correlated to ρ(O3), while air pressure was significantly negatively correlated to ρ(O3). Relative humidity was negatively correlated to ρ(PM2.5), ρ(PM10), ρ(NO2), ρ(SO2) and φ(CO), while air pressure was positively correlated with these pollutants.

Air pollutant prediction based on ARIMA-WOA-LSTM model

The problem of air pollution has always plagued people's lives, and the management of air pollution cannot be achieved without the prediction and assessment of the concentration of various pollutants. In this paper, we propose a method to accurately predict air pollutants with the aim of ensuring the efficiency of air pollution management. The proposed ARIMA-WOA-LSTM model uses ARIMA to extract the linear part of air pollution data and output the nonlinear part, while the WOA-LSTM model is used to predict the nonlinear part, where the whale algorithm is used to find the perfect hyperparameters for the LSTM, and the objectives of the search include the number of neurons, model learning rate and batch length. To prove the excellence of the model developed in the article ARIMA-WOA-LSTM is compared with ARIMA-LSTM, CEEMDAN-WOA-LSTM, WOA-LSTM, ARIMA, and LSTM. The results show that ARIMA-WOA-LSTM performs better than other models in three aspects: pollutant prediction accuracy, model prediction accuracy, and prediction stability; the combined model also performs much better than the single model in the three aspects; The whale algorithm is excellent for the search of the five hyperparameters in the LSTM, which is important for the error reduction of the model. ARIMA-WOA-LSTM model has high reference for air pollution management.

First evidence and potential sources of novel brominated flame retardants and BDE 209 in the deepest ocean

Organic anthropogenic pollutants reach even the deepest parts of the oceans, i.e., the hadal trenches. We here presented the concentrations, influencing factors, and potential sources of polybrominated diphenyl ethers (PBDEs) and novel brominated flame retardants (NBFRs) in hadal sediments and amphipods from the Mariana, Mussau and New Britain trenches. Results showed that BDE 209 was the dominant PBDEs congener and DBDPE was the dominant NBFRs. No significant correlation was found between TOC contents and PBDEs or NBFRs levels in sediment. Lipid content and body length were the potential important factors affecting variation in pollutant concentrations in the carapace & muscle of amphipods, while the pollution levels of viscera were mainly affected by the sex and lipid content. PBDEs and NBFRs might reach trench surface seawater through long-range atmospheric transport and oceans currents but with little contribution from the Great Pacific Garbage Patch. Determination of carbon and nitrogen isotopes indicated that the pollutants were transported and accumulated in amphipods and sediment via different pathways. PBDEs and NBFRs in the hadal sediments were generally transported via the settling of sediment particles of either marine or terrigenous origin whereas in amphipods they accumulated via feeding on animal carrion through the food web. This is the first study reporting on BDE 209 and NBFR contaminations in hadal settings and provide new insight on influencing factors and sources of PBDEs and NBFRs in the deepest oceans.

Understanding Responses of Atmospheric Pollution and its Variability to Contradicting Nexus of Urbanization–Industrial Emission Control in Haldia, an Industrial City of West Bengal

Poor atmospheric health has been causing millions of deaths globally by attacking the respiratory system. According to the World Health Organization, India has the most heavily polluted cities overall and contributes significantly to the annual particle concentration at a global level. This situation demands intensive and extensive monitoring systems for strategy optimization at various levels of governance to combat health and environmental. These strategies include an efficient air quality control strategy, the management of pollution sources while taking into account both local and national controls, etc. However, inadequacy in monitoring stations, which are primarily targeted at cities, has kept most of the small towns and villages out of the radar. This poses difficulty in assessing pollution levels at the local and regional scales, thereby causing a poor understanding of the intensity and spatio-temporal variability of pollutant concentration. Addressing this issue, an attempt has been made in this study to understand the atmospheric pollution of an industry-based city, Haldia in West Bengal, where all the monitoring stations are situated in industrial centers and function nearby making a larger part unmonitored. Handheld instruments were used to collect field data for five other stations selected mainly from non-industrial areas experiencing rapid detrimental land use conversion. Despite being spatially consistent in higher (2015–2019 mean of 95) and lower Air Quality Index (2015–2019 mean of 65), respectively, in industrial and non-industrial areas, contradicting temporal trend is quite distinct in them. Pollution has increased in areas experiencing rapid LULC change, especially in rapidly urbanized areas, whereas industrial emission controls and implementation of various pollution control strategies, including implementation of pollution base profiling and baseline policy action, graded response action plan, etc., have been successful in abating pollution in areas near industries. The study pinpoints the requirement of spatially consistent awareness in rapidly urbanized sections of Haldia to avoid possible environmental hazards caused by urbanization and the necessity of having a dense network of monitoring stations to capture local variations in pollutant concentrations for a better understanding of atmospheric health.

A method for calibrating measurement data of a micro air quality monitor based on MLR-BRT-ARIMA combined model.

A micro air quality monitor can realize grid monitoring and real-time monitoring of air pollutants. Its development can effectively help human beings to control air pollution and improve air quality. However, affected by many factors, the measurement accuracy of micro air quality monitors needs to be improved. In this paper, a combined calibration model of Multiple Linear Regression, Boosted Regression Tree and AutoRegressive Integrated Moving Average model (MLR-BRT-ARIMA) is proposed to calibrate the measurement data of the micro air quality monitor. First, the very widely used and easily interpretable multiple linear regression model is used to find the linear relationship between various pollutant concentrations and the measurement data of the micro air quality monitor to obtain the fitted values of various pollutant concentrations. Second, we take the measurement data of the micro air quality monitor and the fitted value of the multiple regression model as the input, and use the boosted regression tree to find the nonlinear relationship between the concentrations of various pollutants and the input variables. Finally, the autoregressive integrated moving average model is used to extract the information hidden in the residual sequence, and finally the establishment of the MLR-BRT-ARIMA model is completed. Root mean square error, mean absolute error and relative mean absolute percent error are used to compare the calibration effect of the MLR-BRT-ARIMA model and other commonly used models such as multilayer perceptron neural network, support vector regression machine and nonlinear autoregressive models with exogenous input. The results show that no matter what kind of pollutant, the MLR-BRT-ARIMA combined model proposed in this paper has the best performance of the three indicators. Using this model to calibrate the measurement value of the micro air quality monitor can improve the accuracy by 82.4-95.4%.

Characterisation of ambient air quality over two urban sites on the South African Highveld

Air pollution is an environmental-health challenge that many developing and developed countries are struggling to deal with. The purpose of our study was to characterise the ambient air quality during the year 2019–2020 over two urban sites of DiepKloof and Vanderbijilpark which are located on the South African Highveld. The study characterised PM10, PM2.5, SO2, NO2 and O3. Findings were that PM10, PM2.5, SO2 and NO2 ambient concentrations predominantly peaked during the winter season in June, July and August with mean concentrations of 48.6 µg/m3, 29.2 µg/m3, 38.6 µg/m3 and 12.6 µg/m3, respectively for the DiepKloof site while for the Vanderbijilpark site, winter mean for PM10, PM2.5 and SO2 were recorded at 124 µg/m3, 50.7 µg/m3 and 22.1 µg/m3, respectively. The average daily pollutant concentrations of the majority of pollutants were elevated above the WHO air quality guideline limits. Analysis of variance showed the seasonal variations had a significant (all p-values less than 0.01) effect on the variability of pollutant concentrations in both sites. The study attributes these elevated ambient pollutant levels to industrial, domestic and vehicular emission sources that are scattered over the South African Highveld. The study also highlighted the impact of the first lockdown period in South Africa indicating that the first three-week lockdown had a significant (p-values less than 0.001) impact on the variability of pollutant concentrations in both Diep Kloof and Vanderbijlpark. The study also established that pollutant concentrations were more significantly affected by wind direction in the more industrialized Vanderbijlpark than in Diep Kloof. The study contributes towards air quality monitoring and management policy formulation both in South Africa and other African countries. Furthermore, the study contributes towards the academic body of knowledge. To curtail pollutant emissions governments have several options including tightening the air quality regulatory frameworks as well as establishing and supporting community-based air quality advocates. Air quality advocates can act as a conduit in raising general public awareness regarding the impact of air pollution on human health.

Using Mobile Monitoring and Atmospheric Dispersion Modeling for Capturing High Spatial Air Pollutant Variability in Cities

Air pollution is still one of the biggest environmental threats to human health on a global scale. In urban environments, exposure to air pollution is largely influenced by the activity patterns of the population as well as by the high spatial and temporal variability in air pollutant concentrations. Over the last years, several studies have attempted to better characterize the spatial variations in air pollutant concentrations within a city by deploying dense, fixed as well as mobile, low-cost sensor networks and more recently opportunistic sampling and by improving the spatial resolution of air quality models up to a few meters. The purpose of this work has been to investigate the use of properly designed mobile monitoring campaigns along the streets of an urban neighborhood to assess the capability of an operational air dispersion model as SIRANE at the district scale to capture the local variability of pollutant concentrations. To this end, an IoT ecosystem—MONICA (an Italian acronym for Cooperative Air Quality Monitoring), developed by ENEA, has been used for mobile measurements of CO and NO2 concentration in the urban area of the City of Portici (Naples, Southern Italy). By comparing the mean concentrations of CO and NO2 pollutants measured by MONICA devices and those simulated by SIRANE along the urban streets, the former appeared to exceed the simulated ones by a factor of 3 and 2 for CO and NO2, respectively. Furthermore, for each pollutant, this factor is higher within the street canyons than in open roads. However, the mobile and simulated mean concentration profiles largely adapt, although the simulated profiles appear smoother than the mobile ones. These results can be explained by the uncertainty in the estimation of vehicle emissions in SIRANE as well as the different temporal resolution of measurements of MONICA able to capture local high concentrations.

Developing Machine Learning Techniques to Investigate the Impact of Air Quality Indices on Tadawul Exchange Index

The air quality index (AQI) can be described using different pollutant indices. Many investigators study the effect of stock prices and air quality in the field to show if there is a relationship between changing the stock market and the concentration of various pollutants. This study aims to find a relationship between Saudi Tadawul All Share Index (TASI) and multiple pollutants, including particulate matter (PM10), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and AQI. Based on tree models, the relationship is created using linear regression and two prediction models, Chi-square Automatic Interaction Detection (CHAID), and CR-Tree. In order to achieve the target of this research, the TASI dataset relates to six pollutants using time; afterward, the new dataset is divided into three parts—test, validate, and train—after eliminating the outlier data. In order to test the performance of two prediction models, R2 and various error functions are used. The linear regression model results found that PM10, NO2, CO, month, day, and year are significant, whereas O3, SO2, and AQI indices are insignificant. The test dataset showed that R2 scores are 0.995 and 0.986 for CR-Tree and CHAID, respectively, with RMSE values of 387 and 227 for CR-Tree and CHAID, respectively. The prediction models showed that the CHAID model performed better than CR-Tree because it used only three indices, namely, PM10, AQI, and O3, with year and month. The results indicated an effect between changing TASI and the three pollutants, PM10, AQI, and O3.

Did unprecedented air pollution levels cause spike in Delhi's COVID cases during second wave?

The onset of the second wave of COVID-19 devastated many countries worldwide. Compared with the first wave, the second wave was more aggressive regarding infections and deaths. Numerous studies were conducted on the association of air pollutants and meteorological parameters during the first wave of COVID-19. However, little is known about their associations during the severe second wave of COVID-19. The present study is based on the air quality in Delhi during the second wave. Pollutant concentrations decreased during the lockdown period compared to pre-lockdown period (PM2.5: 67 µg m−3 (lockdown) versus 81 µg m−3 (pre-lockdown); PM10: 171 µg m−3 versus 235 µg m−3; CO: 0.9 mg m−3 versus 1.1 mg m−3) except ozone which increased during the lockdown period (57 µg m−3 versus 39 µg m−3). The variation in pollutant concentrations revealed that PM2.5, PM10 and CO were higher during the pre-COVID-19 period, followed by the second wave lockdown and the lowest in the first wave lockdown. These variations are corroborated by the spatiotemporal variability of the pollutants mapped using ArcGIS. During the lockdown period, the pollutants and meteorological variables explained 85% and 52% variability in COVID-19 confirmed cases and deaths (determined by General Linear Model). The results suggests that air pollution combined with meteorology acted as a driving force for the phenomenal growth of COVID-19 during the second wave. In addition to developing new drugs and vaccines, governments should focus on prediction models to better understand the effect of air pollution levels on COVID-19 cases. Policy and decision-makers can use the results from this study to implement the necessary guidelines for reducing air pollution. Also, the information presented here can help the public make informed decisions to improve the environment and human health significantly.

Investigating the Variation of Benzene and 1,3-Butadiene in the UK during 2000-2020.

The concentrations of benzene and 1,3-butadiene in urban, suburban, and rural sites of the U.K. were investigated across 20 years (2000–2020) to assess the impacts of pollution control strategies. Given the known toxicity of these pollutants, it is necessary to investigate national long-term trends across a range of site types. We conclude that whilst legislative intervention has been successful in reducing benzene and 1,3-butadiene pollution from vehicular sources, previously overlooked sources must now be considered as they begin to dominate in contribution to ambient pollution. Benzene concentrations in urban areas were found to be ~5-fold greater than those in rural areas, whilst 1,3-butadiene concentrations were up to ~10-fold greater. The seasonal variation of pollutant concentration exhibited a maximum in the winter and a minimum in the summer with summer: winter ratios of 1:2.5 and 1:1.6 for benzene and 1,3-butadiene, respectively. Across the period investigated (2000–2020), the concentrations of benzene decreased by 85% and 1,3-butadiene concentrations by 91%. A notable difference could be seen between the two decades studied (2000–2010, 2010–2020) with a significantly greater drop evident in the first decade than in the second, proving, whilst previously successful, legislative interventions are no longer sufficiently limiting ambient concentrations of these pollutants. The health impacts of these pollutants are discussed, and cancer impact indices were utilized allowing estimation of cancer impacts across the past 20 years for different site types. Those particularly vulnerable to the adverse health effects of benzene and 1,3-butadiene pollution are discussed.

Analysis of changes and trends in ambient air pollutant concentrations in the Beijing-Tianjin-Hebei region in recent years

The Beijing-Tianjin-Hebei region is a key prevention and control area for air pollution in China. Based on the PM2.5, PM10, SO2, NO2 and AQI data published by the Ministry of Ecology and Environment and the online monitoring and analysis platform of air quality, the temporal variation of air pollutant concentrations in the Beijing-Tianjin-Hebei region from 2019 to 2021 and the contribution of each pollutant to air pollution were analyzed by the comprehensive pollution index method and the pollution load factor method. The results show that: (1) The changes in the comprehensive air pollution index and AQI index in the Beijing-Tianjin-Hebei region from 2019 to 2021 show a downward trend. Compared with the annual average concentrations of PM2.5, PM10, SO2 and NO2 in 2019, the average concentrations of PM2.5, PM10, SO2 and NO2 decreased by 23.72%, 19.28%, 34.28% and 24.03%, respectively. (2) The monthly average change of the concentration of various pollutants in the Beijing-Tianjin-Hebei region from 2019 to 2021 shows a roughly shaped “U”, following the governing rule of "high in winter and low in summer, rising in autumn and falling in spring". (3) The average pollution load coefficients of four pollutants in the Beijing-Tianjin-Hebei region, including PM2.5, PM10, SO2 and NO2, were 38.18%, 31.64%, 3.84% and 26.34% from 2019 to 2021, respectively. It indicates that the ambient air quality in this area is greatly affected by particulate matter pollution.

Association between out-patient visits and air pollution in Chiang Mai, Thailand: Lessons from a unique situation involving a large data set showing high seasonal levels of air pollution.

Chiang Mai is one of the most known cities of Northern Thailand, representative for various cities in the East and South-East Asian region exhibiting seasonal smog crises. While a few studies have attempted to address smog crises effects on human health in that geographic region, research in this regard is still in its infancy. We exploited a unique situation based on two factors: large pollutant concentration variations due to the Chiang Mai smog crises and a relatively large sample of out-patient visits. About 216,000 out-patient visits in the area of Chiang Mai during the period of 2011 to 2014 for upper (J30-J39) and lower (J44) respiratory tract diseases were evaluated with respect to associations with particulate matter (PM10), ozone (O3), and nitrogen dioxide (NO2) concentrations using single-pollutant and multiple-pollutants Poisson regression models. All three pollutants were found to be associated with visits due to upper respiratory tract diseases (with relative risks RR = 1.023 at cumulative lag 05, 95% CI: 1.021–1.025, per 10 μg/m3 PM10 increase, RR = 1.123 at lag 05, 95% CI: 1.118–1.129, per 10 ppb O3 increase, and RR = 1.110 at lag 05, 95% CI: 1.102–1.119, per 10 ppb NO2 increase). Likewise, all three pollutants were found to be associated with visits due to lower respiratory tract diseases (with RR = 1.016 at lag 06, 95% CI: 1.015–1.017, per 10 μg/m3 PM10 increase, RR = 1.073 at lag 06, 95% CI: 1.070–1.076, per 10 ppb O3 increase, and RR = 1.046 at lag 06, 95% CI: 1.040–1.051, per 10 ppb NO2 increase). Multi-pollutants modeling analysis identified O3 as a relatively independent risk factor and PM10-NO2 pollutants models as promising two-pollutants models. Overall, these results demonstrate the adverse effects of all three air pollutants on respiratory morbidity and call for air pollution reduction and control.