Air Quality Trends Research Articles

Investigation of Changes in Atmospheric Pollutants due to the Cessation of Anthropogenic Activities: Spatial Heterogeneity and Complex Atmospheric Chemistry

The current study examines the air quality trends in response to Covid-19-induced lockdowns at various locations in Delhi. The primary pollutants like NO2, CO, and PM10 have shown reductions during the lockdown phase, but the magnitude varied significantly in different places. Also, during the lockdown, air quality in some areas of Delhi exceeded National Ambient Air Quality Standards. Secondary pollutants like O3 have shown mixed trends due to complex atmospheric processes and dependence on relative proportions of VOC and NOx levels. A total of six sites, including traffic, industrial, and residential sites, have been studied. The diurnal behavior of pollutants also differed significantly around different places. During the lockdown, Ashok Vihar, a traffic-influenced area, showed a decrease in O3 (~ 40%), while at DTU (Traffic site), O3 levels increased (~ 48%). The industrial sites Okhla and Wazirpur also showed different trends during the lockdown; O3 in Wazirpur decreased by 50%, whereas Okhla increased by 25%. NOx concentration was lesser in 2020 at all the stations compared to 2019, indicating the positive impact of the lockdown on air pollution due to vehicular emissions. The Approximate Envelope Method estimates the secondary fraction of PM2.5. This fraction of PM is dominated in the lockdown year in the residential site, while it remains unchanged in the traffic site and increased by 11% in the industrial area. Despite being not so far from each other, these sites show very different patterns of pollutants during lockdown episodes.

Application of Kolmogorov-Zurbenko filter to quantify the long-term meteorological and emission impacts on air quality

High concentrations of Particulate Matter (PM) have become a major problem in Turkey because of its economic development over the past decades, as well as its geographical proximity to natural dust source areas. In this study, PM10 data (for the period of 2010–2020) of 36 ground-based stations in 12 metropolitan cities over different regions in Turkey was used and Kolmogorov-Zurbenko (KZ) filter was implemented to decompose the data into its temporal components. A stepwise Multiple Linear Regression (MLR) model was developed to establish relationships between PM10 concentrations and a set of meteorological variables for each city to quantify the long-term meteorological as well as emission impacts on changes and trends in air quality. In this study analysis has revealed that out of twelve major cities in Turkey, only three of them has PM10 levels below or around 45 μgm−3,which is 24 h Air Quality Guideline (AQG) level identified by WHO (WHO, 2021). Overall, over the selected period, long-term change in PM10 concentration is negative for 10 out of 12 cities, ranging between −39.6 μgm−3 and −1.2 μgm−3. Only Adana and Kayseri have a positive long-term change ranging between 1.0 μgm−3 and 3.1 μgm−3. Long-term change in meteorology related component (χLTmet) is relatively constant, hence, long-term change in PM10 concentration (χLT) is heavily influenced by emission related component (χLTemis). As emission impact reduces over time, there is a decrease in PM10 levels in most of the cities. This finding is further supported by reductions in national emissions data.

How the new climate scenarios will affect air quality trends: An exploratory research

In a changing climate, climate scenarios are an essential part of air quality research and assessment. The main goal of this work is to provide an exploratory analysis of the implications of CMIP6 scenarios on air quality, focused on the three high-priority scenarios of IPCC Sixth Assessment Report: SSP2–4.5, SSP3–7.0 and SSP5–8.5. In addition, the similarities and differences between the meteorological and air quality trends from CMIP5 and CMIP6 scenarios were assessed. For that, high-resolution simulations were conducted, having as case study mainland Portugal. The results revealed that it is not possible to establish a clear trend of greater/lesser severity between RCPs and SSPs analogous scenarios, with the similarities (and/or differences) depending on the climate variable and its location in space and time. In addition, due to the narratives that underlies SSPs it is not feasible to establish a direct link between climate and air quality impacts. This happens, since unlike RCPs, SSPs do not show a direct relationship between radiative forcing and the severity of climate impacts. Finally, SSP3–7.0 scenario imposes climate impacts close to those obtained by SSP5–8.5, promoting an overall increase of main air pollutants concentrations (comparing to a reference scenario). Further steps for the research community will involve regional numerical modelling across different areas for use in air quality studies, including a closer look at health implications.

Use of Kolmogorov-Zurbenko Filter Technique to Evaluate PM2.5 Air Quality Trends in New Jersey

This study examined the long-term trends of PM2.5 in the three counties, Camden, Bergen, and Middlesex in New Jersey between 2000 and 2022 using three different averaging methods: USEPA's 3-year average of highest annual average (TYAHAA), simple annual average, and KZ filter rolling average technique. As results, winter and summer had higher PM2.5 levels than spring and fall, which could be attributed to weather patterns and emission changes. For Camden County, an industrialized area, the KZ (-43%) and the TYAHAA (-41%) had a robust agreement. This suggests both KZ and the TYAHAA method could be used to evaluate long term trends for industrialized areas. As to Bergen County, an urban residential area with intensive transportation sources, the KZ (-29%) showed a disagreement with the TYAHAA (-41%) but correlated with the simple annual average (-25%). This suggests that the KZ is better at capturing transportation-related short-term fluctuations than the TYAHAA. However, the KZ average (+9%) had a significant discrepancy from the TYAHAA (-41%) for a suburban site. These discrepancies may be due to various factors, including differences in emission sources, meteorological conditions, emission control measures, and the time period analyzed. In conclusion, the KZ method can be useful for identifying and removing short-term fluctuations in data to reveal underlying long-term trends in PM2.5 air quality. However, the choice of method should be based on the specific objectives of the study and the nature of the data. Using the three methods may provide a more comprehensive assessment of air quality trends.

Short-term exposure of PM2.5 and PM10 increases the number of outpatients with eczema in Guangzhou: A time-series study.

The worldwide prevalence of eczema has continued to rise over the past decades. This has led to the emphasis on the association between air pollution and eczema. This study investigated the relationship between daily exposure to air pollution and the number of eczema outpatient visits in Guangzhou with the overarching goal of providing novel insights on the interventions for eczema aggravation and prevention. Daily air pollution data, meteorological data, and the number of eczema outpatients were obtained from 18 January 2013 to 31 December 2018 in Guangzhou. A generalized additive model with Poisson distribution was used to assess the association between the number of eczema outpatient visits and short-term exposure to PM2.5 and PM10. In addition, the association of PM2.5 and PM10 by age (<65 years, ≥65 years) and gender was evaluated. A total of 293,343 eczema outpatient visits were recorded. The obtained results indicated that a 10 μg/m3 increase of the same day/lag 1 day/lag 2 days PM2.5 was associated with increments of 2.33%, 1.81%, and 0.95% in eczema outpatient risk, respectively. On the other hand, a 10 μg/m3 increase of PM10 was associated with eczema outpatients risk increments of 1.97%, 1.65%, and 0.98% respectively. Furthermore, the associations of PM on the increment of eczema were similar in the male and female groups. Results obtained after age stratified analyses indicated that the strongest positive association between PM2.5 exposure and eczema was observed at lag 0 day with the percent changes being 4.72% and 3.34% in <12 years old, ≥12 and <65 years old, and ≥65 years old groups, respectively. Short-term exposure to PM2.5 and PM10 increases the number of eczema outpatients, especially among children and the elderly. The relationship between air quality trends and hospital resource arrangement should be paid attention to by hospital managers which may aid in disease prevention and lower the health burden.

The AirGAM 2022r1 air quality trend and prediction model

Abstract. This paper presents the AirGAM 2022r1 model – an air quality trend and prediction model developed at the Norwegian Institute for Air Research (NILU) in cooperation with the European Environment Agency (EEA) over 2017–2021. AirGAM is based on nonlinear regression GAMs – generalised additive models – capable of estimating trends in daily measured pollutant concentrations at air quality monitoring stations, discounting for the effects of trends and time variations in corresponding meteorological data. The model has been developed primarily for the compounds NO2, O3, PM10, and PM2.5. Meteorological input data consist of temperature, wind speed and direction, planetary boundary layer height, relative and absolute humidity, cloud cover, and precipitation over the period considered. The exact set of meteorological variables used in the model depends on the compound selected for analysis. In addition to meteorological variables introduced in the model as covariates, i.e. explanatory variables for the concentration levels, the model also incorporates time variables such as the day of the week, day of the year, and overall time, which is related to the model's trend term. The trend analysis is performed at each station separately. Thus, the model only considers the temporal features of concentrations and meteorology at a station, rather than any spatial correlations or dependencies between stations. AirGAM is implemented using the R language for statistical computing and, in particular, the GAM package mgcv. In the model, meteorological and time covariates are represented and estimated as smooth nonlinear functions of the corresponding variables. Thus, the trend term is defined and estimated as a smooth nonlinear function of time over the period selected for analysis. Once fitted to training data, the model may be used as a prediction tool capable of predicting air pollutant concentrations for new sets of meteorological and time data which are not in the training set – e.g. for cross-validation or forecasting purposes. The model does not explicitly use emissions or background concentrations – these are sought to be implicitly represented through the estimated nonlinear relations between meteorology, time, and concentrations. In addition to meteorology-adjusted trends, the program also produces unadjusted trends – i.e. trends based on the same regression set-up but only including the time covariates. Both types of trends can be output in the same run, making it possible to compare them. Ideally, the meteorology-adjusted trend will show the trend in concentration mainly due to changes in emissions or physicochemical processes not induced by changes in meteorology. AirGAM has been developed and tested primarily in trend studies based on measurement data hosted by the EEA, including the AirBase data (before 2013) and the Air Quality e-Reporting (AQER) data from 2013 and onwards. Still, the model is general and could be applied in other regions with other input data. The EEA data provide daily or hourly surface measurements at individual monitoring stations in Europe. For input meteorological data, we extract time series from the gridded meteorological re-analysis (ERA5) provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) for each monitoring station. The paper presents results with the model for all AirBase/AQER stations in Europe from the latest EEA trend study for 2005–2019.

Air Quality Integrated Assessment: Environmental Impacts, Risks and Human Health Hazards

The monitoring and evaluation of air quality is a topic of great global interest as, with the decline of air quality, there are negative effects on human health and ecosystems. Thus, the purpose of this paper was to evaluate the air quality over 11 years, in the period 2011–2021, in four cities in Romania, reported as most polluted, namely, Brasov, Cluj-Napoca, Iasi, and Timisoara. Pollutants of interest included arsenic, carbon monoxide, and PM2.5. The measured concentrations of the selected pollutants were collected from the National Environmental Protection Agency public reports. The database considered the daily measurements for the selected pollutants, from three monitoring stations in each city so that the air quality and trends for the last 11 years and impact assessment could be developed. Therefore, the input data were statistically analyzed to identify the trends of air quality, and then, on this basis, the environmental impacts and risks and health hazards were quantified. High concentrations of PM2.5 were recorded for Iasi city, while for Timisoara city, significant concentrations of arsenic were reported. The results regarding the air quality aggregate index, air pollution index, and health hazard index were in the regular range, but in the case of sensitive, vulnerable targets such as children, they were triple compared to adults. The results show that the alert threshold value for PM2.5 was exceeded every year in all four cities, while in the case of Timisoara city, the arsenic air pollution proved to be at a significant level with a major risk for human health.

Evaluating the Performance of Low-cost PM Sensors over Multiple COALESCE Network Sites

Air quality is a global concern, with particulate matter receiving considerable attention due to its impact on human health and climate change. Recent advances in low-cost sensors allow their deployment in large number to measure spatio-temporal and real-time air quality data. Low-cost sensors need careful evaluation with both regulatory approved methods and other data sets to understand their efficacy. In this work, PM concentrations measured by deploying low-cost sensors at four regional sites are evaluated through comparison with satellite-based model MERRA-2 and the SASS reference instrument. Daily PM2.5 mass concentration variation was analyzed at four regional sites of India from January 2020 to July 2020, including pre-lockdown and six different lockdown periods. Higher PM2.5 concentration was observed at Rohtak (119 µg/m3) compared to Mahabaleshwar (33 µg/m3), Bhopal (45 µg/m3) and Kashmir sites during the pre-lock down period. During the lockdown period, the PM2.5 mass concentration was reduced significantly compared to the pre-lockdown period at every location, although the PM2.5 concentration was different at each location. The air quality trend was quite similar in both the measurements, however, MERRA-2 reconstructed PM2.5 was significantly lower in the pre-lockdown period compared to the lockdown periods. Significant differences were observed between low-cost sensor measurements and MERRA-2 reanalysis data. These are attributed to the MERRA-2 modelling analysis that measures less PM2.5 concentration as compared to ground-based measurements, whereas low-cost sensor are ground-based measurements but needs corrections as it is subject to the calibration dependencies and biases.

Temporal and spatial variation characteristics of air quality trends in China

Temporal and spatial variation characteristics of air quality trends in China

An Empirical Mode Decomposition Fuzzy Forecast Model for Air Quality.

Air quality has a significant influence on people's health. Severe air pollution can cause respiratory diseases, while good air quality is beneficial to physical and mental health. Therefore, the prediction of air quality is very important. Since the concentration data of air pollutants are time series, their time characteristics should be considered in their prediction. However, the traditional neural network for time series prediction is limited by its own structure, which makes it very easy for it to fall into a local optimum during the training process. The empirical mode decomposition fuzzy forecast model for air quality, which is based on the extreme learning machine, is proposed in this paper. Empirical mode decomposition can analyze the changing trend of air quality well and obtain the changing trend of air quality under different time scales. According to the changing trend under different time scales, the extreme learning machine is used for fast training, and the corresponding prediction value is obtained. The adaptive fuzzy inference system is used for fitting to obtain the final air quality prediction result. The experimental results show that our model improves the accuracy of both short-term and long-term prediction by about 30% compared to other models, which indicates the remarkable efficacy of our approach. The research of this paper can provide the government with accurate future air quality information, which can take corresponding control measures in a targeted manner.

Air pollution risk assessment related to fossil fuel-driven vehicles in megacities in China by employing the Bayesian network coupled with the Fault Tree method

Vehicle emissions have become one of the key pollution sources affecting air quality and human health in China's megacities. How to curb excess vehicle emissions has become a key pain point of urban air pollution prevention and control. This work tries to explore an effective integrated approach/framework to quantitatively assess the risk factors of excess vehicle emissions (EVE) and their impact on air quality for China's typical megacities. Bayesian Network is employed as the assessment tool by coupling with the Fault Tree method to curb the above problem for the first time. Four megacities (Beijing, Tianjin, Hangzhou, and Guangzhou) in China are selected as case studies, and the risk factors leading to EVE are identified to construct the Bayesian Network model. At the same time, some accurate quantisation algorithms of the occurrence probability of root nodes were proposed for the target megacities from 2014 to 2019. The analysis results show that the variation trend of the probability of EVE has a good positive correlation with the variation trend of air quality in some megacities. From 2014 to 2019, the no-occurrence probability of EVE in Beijing, Tianjin, and Hangzhou increased from 0.4972, 0.4973, and 0.6314 to 0.6491, 0.6846, and 0.7564; the good air quality rate increased from 47.1%, 47.9%, and 59.2%–65.8%, 60%, and 78.6%. Based on the developing trend of the historical data/information and considering the impact of new energy vehicles, the no-occurrence probability of EVE in Beijing and Tianjin is predicted to be increased from 0.6888 to 0.7929 in 2020 to 0.8561 and 0.8645 in 2025. This work may provide a novel approach and perspective that can realise accurate traceability of key risk factors, quantitative risk assessment and prediction function of urban vehicle emissions for sustainable development of China's megacities.

Air Quality Characteristics and Their Natural and Socioeconomic Drivers in Beijing-Tianjin-Hebei Economic Zone, Northern China

Beijing-Tianjin-Hebei Economic Zone is one of the most polluted areas in northern China. In recent years, pollution has become one of the most severe the environmental problems in that region, posing huge impact on the urban traffi c and threatening the public health greatly. Understanding the spatio-temporal variability and their mechanisms is important for the monitoring and control of regional air pollution. This study employed the Mann-Kendall test, statistical methods, geographical spatial analysis, aiming to explore the characteristics of air quality and its main controlling factors in this region. Temporal analysis was conducted in four largest cities to indicate the variation trend of air quality and identify the infl uence of meteorological factors, while spatial analysis was conducted based on all 13 cities of the region to clarify the socioeconomic controls on air quality. The results from trend analysis indicate an improving trend in air quality. Temperature, air pressure and wind speed are the main meteorological factors controlling air quality in the four large cities, which impose important infl uence on the migration, diffusion or settlement of pollutants. Agglomeration degree and distribution characteristics of AQI and six pollutants show great spatial heterogeneity, which is determined by topography, land-sea position, physicochemical properties of pollutants and socioeconomic factors. Principal component analysis (PCA) indicate that economic development benefi ts the improvement of air quality, while industrialization and traffi c construction aggravate air pollution. The air quality is less dependent on the level of urbanization. About 55 % of the spatial AQI changes can be explained by the socioeconomic factors, suggesting the leading contribution of human activities to air quality. This study will help deeply understand the main sources of air pollution in Beijing-Tianjin-Hebei region, and provides insights for urban planning and rational policy formulation that are conducive to air pollution control.

Comprehensive Evaluation of Environmental Air Quality Based on the Entropy Weights and Concentration Variation Trends of Pollutants

The comprehensive index method has difficulties in evaluating the influence of air pollutant concentration changes on ambient air quality. Thus, a comprehensive evaluation method based on pollutant entropy weights and trend-regulating factors is proposed. According to the information entropy rates of 6 pollutants, the single entropy weight index is proposed by integrating it with the single-quality index, which reflects pollutant variations in evaluation periods. The Spearman’s rank correlation coefficient between the pollutant and Air Quality Index (AQI) is defined as the trend-regulating factor, which indicates the correlations between pollutants and improvements or retrogressions in ambient air quality. The covariance is used to determine the variation trend of ambient air quality, which decides the positive or negative of trend-regulating factor. This method is used to study the ambient air quality rates in 10 cities of Shaanxi Province from 2017 to 2022. The trends of air quality improvements vary among the central, northern, and southern cities. The central cities have more spaces for air quality improvements in terms of PM2.5 and O3. Although prevention efforts have reduced the impacts of pollutants, PM2.5 is still the key factor affecting improvements in ambient air quality in most cities in winter. Additionally, the O3 pollution in summer was not controlled effectively. The contribution to air pollution of O3 increased, on the contrary with the improvement in air quality. The coordinated control of PM2.5 and O3 is still an important method of ambient air quality improvement.

Assessing short term air quality trend in Malaysia based on air pollution index (APi)

Air Pollution Index (API) is used in Malaysia to determine the daily air quality status, which is calculated based on the daily concentrations of particulate matter (PM10), ground-level ozone (O3), carbon monoxide (CO), sulphur dioxide (SO2) and nitrogen dioxide (NO2). This study presents short-term air quality trends based on API from the 52 air quality monitoring stations nationwide between 2010 and 2016. The air quality data and meteorological conditions were obtained from the Department of Environment and used for the API calculation. The API value is classified into six categories, namely: Good (0-50), Moderate (51-100), Unhealthy (101-200), Very Unhealthy (201-300), Hazardous (301-500), and Emergency (above 500). The coefficient of variation (CV) and Mann-Kendall trend test (MK) were used to assess the API variation and trend in each air quality monitoring station. Between the study periods, the API values were largely varied. Observation at 32 air quality monitoring stations have shown significant but small increasing trends, while 12 stations showed significant decreasing trends, and the remaining 8 stations showed no significant trends. The frequency of exceedance (API>50) was used to assess the percentages of unhealthy days. The analysis has found that air quality in Klang Valley was experiencing the highest number of unhealthy days, while the two Malaysian states in Borneo (Sabah and Sarawak) to be relatively less polluted.

Wildfire activity is driving summertime air quality degradation across the western US: a model-based attribution to smoke source regions

Over recent decades, wildfire activity across western North America has increased in concert with summertime air quality degradation in western US urban centers. Using a Lagrangian atmospheric modeling framework to simulate smoke transport for almost 20 years, we quantitatively link decadal scale air quality trends with regional wildfire activity. Modeled smoke concentrations correlate well with observed fine-mode aerosol (PM2.5) concentrations (R > 0.8) at the urban centers most impacted by smoke, supporting attribution of observed trends to wildfire sources. Many western US urban centers (23 of 33 total) exhibit statistically significant trends toward enhanced, wildfire-driven, extreme (98th quantile) air quality episodes during the months of August and September for the years 2003–2020. In the most extreme cases, trends in 98th quantile PM2.5 exceed 2 μg m−3 yr−1, with such large trends clustering in the Pacific Northwest and Northern/Central California. We find that the Pacific Northwest is uniquely impacted by smoke from wildfires in the mountainous Pacific Northwest, California, and British Columbia, leading to especially robust degradation of air quality. Summertime PM2.5 trends in California and the Intermountain West are largely explained by wildfires in mountainous California and the American Rockies, respectively. These results may inform regional scale forest management efforts, and they present significant implications for understanding the wildfire—air quality connection in the context of climate driven trends toward enhanced wildfire activity and subsequent human exposure to degraded air quality.

Air quality assessment based on R/S-SPA composite model: A case study of the Hebei province (China).

Air pollution has been the favorite subject of pollution prevention and control measures with the aim of sustainable development. Therefore, a composite model of rescaled range (R/S) and set pair analysis (SPA) was constructed to evaluate the air pollution situation based on the data from 2013 to 2020 in the Hebei province and then predict its air quality development trend. The results show that (1) the air pollution situation is severe, but the development trend is good and (2) the main pollutants are the core factors affecting air quality, and it is necessary to focus on intervention. The innovation of this paper lies on the combination of R/S and SPA to jointly predict the development trend of air quality in the Hebei province and ensure its scientific prediction. Meanwhile, this paper does not point out the continuation cycle of development state, which is the limitation of this paper. Finally, this study deepens the understanding of air quality evaluation, and the following countermeasures are formed: adherence to the problem- and goal-oriented approach to help the transformation of low-carbon development and enhancing the awareness of energy conservation and environmental protection to practice low-carbon lifestyle.

Geospatial Technology-Based Analysis of Air Quality in India during the COVID-19 Pandemic

The study evaluates the impacts of India’s COVID-19 lockdown and unlocking periods on the country’s ambient air quality. India experienced three strictly enforced lockdowns followed by unlocking periods where economic and social restrictions were gradually lifted. We have examined the in situ and satellite data of NO2 emissions for several Indian cities to assess the impacts of the lockdowns in India. Additionally, we analyzed NO2 data acquired from the Sentinel-5P TROPOMI sensor over a few districts of the Punjab state, as well as the National Capital Region. The comparisons between the in situ and satellite NO2 emissions were performed for the years 2019, 2020 and up to July 2021. Further analysis was conducted on the satellite data to map the NO2 emissions over India during March to July for the years of 2019, 2020 and 2021. Based on the in situ and satellite observations, we observed that the NO2 emissions significantly decreased by 45–55% in the first wave and 30% in the second wave, especially over the Northern Indian cities during the lockdown periods. The improved air quality over India is indicative of reduced pollution in the atmosphere due to the lockdown process, which slowed down the industrial and commercial activities, including the migration of humans from one place to another. Overall, the present study contributes to the understanding of the trends of the ambient air quality over large geographical areas using the Sentinel-5P satellite data and provides valuable information for regulatory bodies to design a better decision support system to improve air quality.

Air Pollution Characteristics during the 2022 Beijing Winter Olympics.

Using air pollution monitoring data from 31 January to 31 March 2022, we evaluated air quality trends in Beijing and Zhangjiakou before and after the 2022 Winter Olympics and compared them with the conditions during the same period in 2021. The objective was to define the air quality during the 2022 Winter Olympics. The results indicated that: (1) the average concentrations of PM2.5, PM10, NO2, CO, and SO2 in Zhangjiakou during the 2022 Winter Olympics were 28.15, 29.16, 34.96, 9.06, and 16.41%, respectively, lower than those before the 2022 Winter Olympics; (2) the five pollutant concentrations in Beijing showed the following pattern: during the 2022 Winter Olympics (DWO) < before the 2022 Winter Olympics < after 2022 Winter Paralympics < during 2022 Winter Paralympics; (3) on the opening day (4 February), the concentrations of the five pollutants in both cities were low. PM2.5 and PM10 concentrations varied widely without substantial peaks and the daily average maximum values were 15.17 and 8.67 µg/m3, respectively, which were 65.56 and 69.79% lower than those of DWO, respectively; (4) the PM2.5 clean days in Beijing and Zhangjiakou DWO accounted for 94.12 and 76.47% of the total days, respectively, which were 11.76 and 41.18% higher than those during the same period in 2021; (5) during each phase of the 2022 Winter Olympics in Beijing and Zhangjiakou, the NO2/SO2 and PM2.5/SO2 trends exhibited a decrease followed by an increase. The PM2.5/PM10 ratios in Beijing and Zhangjiakou were 0.65 and 0.67, respectively, indicating that fine particulate matter was the main contributor to air pollution DWO.

Air Quality and Cancer Prevalence Trends across the Sub-Saharan African Regions during 2005-2020.

Poor air quality and environmental pollution remain some of the main etiological factors leading to cancers and cancer-related deaths worldwide. As a result of human activities, deleterious airborne chemicals can be dispersed not only in the environment but also released in occupational environments and industrial areas. Air pollutants and cancer links are now established through various oxidative stress-related mechanisms and related DNA damages. Generally, ambient and indoor air pollutants have been understudied in sub-Saharan Africa (SSA) compared to other regions in the world. Our study not only highlights the deleterious effects of air pollutants in these developing countries, but it has strived to examine the trends and correlations between cancers and some air pollutants—carbon dioxide, other greenhouse gases, PM2.5, and human development index—in some SSA countries, where recent cancer burdens were reported as high. Our results showed strikingly higher yearly trends of cancers and above-mentioned air pollutant levels in some sub-Saharan countries during 2005–2020. Relative risks (RR) of these air pollutants-related cancer case rates were, however, below, or slightly above 1.0, or not statistically significant possibly due to other responsible and confounding factors which were not considered in our analyses due to data unavailability. We recommend new approaches to monitoring, minimizing, and creating awareness of the trends of hazardous air pollutants in sub-Saharan Africa, which will help ameliorate cancer prevalence and support the reduction in air pollution levels within regulatory limits, thereby relieving the cumulative burdens of cancers. Utilization of the findings from the study will support large-scale public health and health policy efforts on cancer management through environmental stewardship in SSA countries having the poorest outcome and the shortest survival rates from cancers.

Air Quality and Traffic Trends in Cincinnati, Ohio during the COVID-19 Pandemic

During 2020, the stay-at-home order mandated in response to the COVID-19 pandemic led to significant changes in traffic volumes in Cincinnati, OH. Air pollutant concentrations (PM2.5, black carbon (BC), carbon monoxide (CO), nitrogen dioxide (NO2), nitrogen dioxide (NOX), and ozone (O3)) monitored at two ground monitoring sites in the city of Cincinnati were analyzed intra-annually in 2020 to quantify if the stay-at-home order impacted air quality. Interannual analyses were also conducted to evaluate differences in 2020 data versus historical years (2016–2019). Traffic volume data were also analyzed, where it was observed that, compared to pre-pandemic 2020, total traffic counts decreased by up to 26.41% during Ohio’s stay-at-home order, while heavy-duty vehicle traffic increased by up to 26.95% during the latter half of 2020. Statistical analysis indicated nonuniform changes in air pollutant concentrations at both sites throughout 2020. During the lockdown period at the central monitoring site, PM2.5 increased by 9%, while NO2 decreased by 30% compared to pre-pandemic concentrations in 2020. For BC and CO, there were no significant changes.