Dataset of air pollutants (PM2.5, PM10, CO) concentrations in the export processing area of Dhaka, Bangladesh.

This study presents a valuable dataset on air quality in the densely populated Dhaka Export Processing Zone (DEPZ) of Bangladesh. It included a dataset of Particulate Matter (PM2.5, PM10) and CO concentrations with Air Quality Index (AQI) values. PM data was collected 24h, and CO data was collected 8h monthly from 2019 to 2023 using respirable dust sampler APS-113NL for PM2.5, APS-113BL for PM10, and LUTRON AQ9901SD Air Quality Monitor Data Logger used to measure CO concentration data. Data sampling locations are selected based on population density, and employment data for DEPZ is also included, highlighting a potential rise in population density. This article also forecasted pollutant concentrations, AQI values, and health hazards associated with air pollutants using the Auto Regressive Moving Average (ARIMA) model. The performance of the ARIMA model was also measured using root mean squared error (RMSE) and mean absolute error (MAE). However, this can be used to raise awareness among the public about the health hazards associated with air pollution and encourage them to take measures to reduce their exposure to air pollutants. In addition, this data can be instrumental for researchers and policymakers to assess air pollution risks, develop control strategies, and improve air quality in the DEPZ.

Characterization of air pollution and associated health risks in Gansu Province, China from 2015 to 2022.

Air pollution poses a major threat to both the environment and public health. The air quality index (AQI), aggregate AQI, new health risk-based air quality index (NHAQI), and NHAQI-WHO were employed to quantitatively evaluate the characterization of air pollution and the associated health risk in Gansu Province before (P-I) and after (P-II) COVID-19 pandemic. The results indicated that AQI system undervalued the comprehensive health risk impact of the six criteria pollutants compared with the other three indices. The stringent lockdown measures contributed to a considerable reduction in SO2, CO, PM2.5, NO2 and PM10; these concentrations were 43.4%, 34.6%, 21.4%, 17.4%, and 14.2% lower in P-II than P-I, respectively. But the concentration of O3 had no obvious improvement. The higher sandstorm frequency in P-II led to no significant decrease in the ERtotal and even resulted in an increase in the average ERtotal in cities located in northwestern Gansu from 0.78% in P-I to 1.0% in P-II. The cumulative distribution of NHAQI-based population-weighted exposure revealed that 24% of the total population was still exposed to light pollution in spring during P-II, while the air quality in other three seasons had significant improvements and all people were under healthy air quality level.

IoT-based monitoring system and air quality prediction using machine learning for a healthy environment in Cameroon.

This paper is aimed at developing an air quality monitoring system using machine learning (ML), Internet of Things (IoT), and other elements to predict the level of particulate matter and gases in the air based on the air quality index (AQI). It is an air quality assessor and therefore a means of achieving the Sustainable Development Goals (SDGs), in particular, SDG 3.9 (substantial reduction of the health impacts of hazardous substances) and SDG 11.6 (reduction of negative impacts on cities and populations). AQI quantifies and informs the public about air pollutants and their adverse effects on public health. The proposed air quality monitoring device is low-cost and operates in real-time. It consists of a hardware unit that detects various pollutants to assess air quality as well as other airborne particles such as carbon dioxide (CO2), methane (CH4), volatile organic compounds (VOCs), nitrogen dioxide (NO2), carbon monoxide (CO), and particulate matter with an aerodynamic diameter of 2.5 microns or less (PM2.5). To predict air quality, the device was deployed from November 1, 2022, to February 4, 2023, in certain bauxite-rich areas of Adamawa and certain volcanic sites in western Cameroon. Therefore, machine learning algorithm models, namely, multiple linear regression (MLR), support vector regression (SVR), random forest regression (RFR), XGBoost (XGB), and K-nearest neighbors (KNN) were applied to analyze the collected concentrations and predict the future state of air quality. The performance of these models was evaluated using mean absolute error (MAE), coefficient of determination (R-square), and root mean square error (RMSE). The obtained data in this study show that these pollutants are present in selected localities albeit to different extents. Moreover, the AQI values obtained range from 10 to 530, with a mean of 132.380 ± 63.705, corresponding to moderate air quality state but may induce an adverse effect on sensitive members of the population. This study revealed that XGB regression performed better in air quality forecasting with the highest R-squared (test score of 0.9991 and train score of 0.9999) and lowest RMSE (test score of 1.5748 and train score of 0. 0073) and MAE (test score of 0.0872 and train score of 0.0020), while the KNN model had the worst prediction (lowest R-squared and highest RMSE and MAE). This embryonic work is a prototype for projects in Cameroon as measurements are underway for a national spread over a longer period of time.

Particulate and gaseous air pollutants exceed WHO guideline values and have the potential to damage human health in Faisalabad, Metropolitan, Pakistan.

First-ever measurements of particulate matter (PM2.5, PM10, and TSP) along with gaseous pollutants (CO, NO2, and SO2) were performed from June 2019 to April 2020 in Faisalabad, Metropolitan, Pakistan, to assess their seasonal variations; Summer 2019, Autumn 2019, Winter 2019-2020, and Spring 2020. Pollutant measurements were carried out at 30 locations with a 3-km grid distance from the Sitara Chemical Industry in District Faisalabad to Bhianwala, Sargodha Road, Tehsil Lalian, District Chiniot. ArcGIS 10.8 was used to interpolate pollutant concentrations using the inverse distance weightage method. PM2.5, PM10, and TSP concentrations were highest in summer, and lowest in autumn or winter. CO, NO2, and SO2 concentrations were highest in summer or spring and lowest in winter. Seasonal average NO2 and SO2 concentrations exceeded WHO annual air quality guide values. For all 4 seasons, some sites had better air quality than others. Even in these cleaner sites air quality index (AQI) was unhealthy for sensitive groups and the less good sites showed Very critical AQI (> 500). Dust-bound carbon and sulfur contents were higher in spring (64 mg g-1) and summer (1.17 mg g-1) and lower in autumn (55 mg g-1) and winter (1.08 mg g-1). Venous blood analysis of 20 individuals showed cadmium and lead concentrations higher than WHO permissible limits. Those individuals exposed to direct roadside pollution for longer periods because of their occupation tended to show higher Pb and Cd blood concentrations. It is concluded that air quality along the roadside is extremely poor and potentially damaging to the health of exposed workers.

Relationship Between Air Pollutants and the Incidence of Epistaxis in Yangzhou.

Objectives: This project aims to explore the relationship between the air quality index (AQI), the concentration of 6 air pollutants, and the incidence of epistaxis in Yangzhou. Also, to provide reference information for the prevention and treatment of epistaxis. Methods: Data of patients with epistaxis admitted to the Northern Jiangsu People's Hospital Affiliated to Yangzhou University from January 2017 to December 2021 were collected. In addition, the local AQI and the concentrations of 6 air pollutants, namely particulate matter (PM2.5, PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3), were analyzed at the time of onset. Furthermore, the correlation with the incidence of epistaxis has been analyzed. Results: From 2017 to 2021, there were 24,721 patients with epistaxis aged from 0 to 17 years old while male patients were more than females. The incidence was higher in April, May, and June. There was a statistically significant difference in the number of daily epistaxis in different months and under AQI conditions (P < .05). Spearman's correlation analysis showed that there was a positive correlation between the number of daily epistaxis and the concentrations of AQI, CO, NO2, O3, PM2.5, PM10, and SO2 in Yangzhou, in which O3, PM10, and SO2 were highly correlated with the average number of daily epistaxis, and there was no obvious time lag effect of air pollutants on epistaxis. Conclusion: Epistaxis in the Yangzhou area is more common in males, mostly occurs in 0 to 17 years old, with seasonal. There was also a positive correlation between the incidence of epistaxis and air pollutants in Yangzhou. Therefore, by reducing the AQI index in daily life, and reducing the concentration of environmental pollutants in the air, the occurrence of epistaxis could be prevented and reduced to a certain extent.

Index Air Quality Monitoring for Light and Active Mobility.

Light and active mobility, as well as multimodal mobility, could significantly contribute to decarbonization. Air quality is a key parameter to monitor the environment in terms of health and leisure benefits. In a possible scenario, wearables and recharge stations could supply information about a distributed monitoring system of air quality. The availability of low-power, smart, low-cost, compact embedded systems, such as Arduino Nicla Sense ME, based on BME688 by Bosch, Reutlingen, Germany, and powered by suitable software tools, can provide the hardware to be easily integrated into wearables as well as in solar-powered EVSE (Electric Vehicle Supply Equipment) for scooters and e-bikes. In this way, each e-vehicle, bike, or EVSE can contribute to a distributed monitoring network providing real-time information about micro-climate and pollution. This work experimentally investigates the capability of the BME688 environmental sensor to provide useful and detailed information about air quality. Initial experimental results from measurements in non-controlled and controlled environments show that BME688 is suited to detect the human-perceived air quality. CO2 readout can also be significant for other gas (e.g., CO), while IAQ (Index for Air Quality, from 0 to 500) is heavily affected by relative humidity, and its significance below 250 is quite low for an outdoor uncontrolled environment.

Association between short- and medium-term exposure to air pollutants and depressive episode using comprehensive air quality index among the population in South Korea.

BACKGROUND: Currently, air pollution is suggested as a risk factor for depressive episodes. Our study aimed to consider multiple air pollutants simultaneously, and continuously evaluate air pollutants using comprehensive air quality index (CAI) with depressive episode risk. METHODS: Using a nationally representative sample survey from South Korea between 2014 and 2020, 20,796 participants who underwent health examination and Patient Depression Questionnaire-9 were included in the study. Six air pollutants (PM10, PM2.5, O3, CO, SO2, NO2) were measured for the analysis. Every air pollutant was standardized by air quality index (AQI) and CAI was calculated for universal representation. Using logistic regression, short- and medium-term exposure by AQI and CAI with the risk of depressive episode was calculated by odds ratio and 95 % confidence interval (CI). Furthermore, consecutive measurements of CAI over 1-month time intervals were evaluated with the risk of depressive episodes. Every analysis was conducted seasonally. RESULTS: There were 950 depressive episodes occurred during the survey. An increase in AQI for short-term exposure (0-30 days) showed higher risk of depressive episode in CO, while medium-term exposure (0-120 days) showed higher risk of depressive episode in CO, SO2, PM2.5, and PM10. During the cold season, the exposure to at least one abnormal CAI within 1-month intervals over 120 days was associated with a 68 % (95 % CI 1.11-2.54) increase in the risk of depressive episode. CONCLUSIONS: Short- and medium-term exposure of air pollution may be associated with an increased risk of depressive episodes, especially for cold season.

A hybrid multi-scale fusion paradigm for AQI prediction based on the secondary decomposition.

With rapid industrialization and urbanization, air pollution has become an increasingly severe problem. As a key indicator of air quality, accurate prediction of the air quality index (AQI) is essential for policymakers to establish effective early warning management mechanisms and adjust living plans. In this work, a hybrid multi-scale fusion prediction paradigm is proposed for the complex AQI time series prediction. First, an initial decomposition and integration of the original data is performed by combining the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and sample entropy (SE). Then, the subsequences, divided into high-frequency and low-frequency groups, are applied to different processing methods. Among them, the variational mode decomposition (VMD) is chosen to perform a secondary decomposition of the high-frequency sequence groups and integrated by using K-means clustering with sample entropy. Finally, multi-scale fusion training of sequence prediction results with different frequencies by using long short-term memory (LSTM) yields more accurate results with R2 of 0.9715, RMSE of 2.0327, MAE of 0.0154, and MAPE of 0.0488. Furthermore, validation of the AQI datasets acquired from four different cities demonstrates that the new paradigm is more robust and generalizable as compared to other baseline methods. Therefore, this model not only holds potential value in developing AQI prediction models but also serves as a valuable reference for future research on AQI control strategies.

Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India.

This research was established to accurately forecast daily scale air quality index (AQI) which is an essential environmental index for decision-making. Researchers have projected different types of models and methodologies for AQI forecasting, such as statistical techniques, machine learning (ML), and most recently deep learning (DL) models. The modelling development was adopted for Delhi city, India which is a major city with air pollution issues simialir to entire urban cities of India especially during winter seasons. This research was predicted AQI using different versions of DL models including Long-Short Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM) and Bidirectional Recurrent Neural Networks (Bi-RNN) in addition to Kernel Ridge Regression (KRR). Results indicated that Bi-RNN model consistently outperformed the other models in both training and testing phases, while the KRR model consistently displayed the weakest performance. The outstanding performance of the models development displayed the requirement of adequate data to train the models. The outcomes of the models showed that LSTM, BI-LSTM, KRR had lower performance compared with Bi-RNN models. Statistically, Bi-RNN model attained maximum cofficient of determination (R2 = 0.954) and minimum root mean square error (RMSE = 25.755). The proposed model in this research revealed the robust predictable to provide a valuable base for decision-making in the expansion of combined air pollution anticipation and control policies targeted at addressing composite air pollution problems in the Delhi city.

Improving dengue fever predictions in Taiwan based on feature selection and random forests.

BACKGROUND: Dengue fever is a well-studied vector-borne disease in tropical and subtropical areas of the world. Several methods for predicting the occurrence of dengue fever in Taiwan have been proposed. However, to the best of our knowledge, no study has investigated the relationship between air quality indices (AQIs) and dengue fever in Taiwan. RESULTS: This study aimed to develop a dengue fever prediction model in which meteorological factors, a vector index, and AQIs were incorporated into different machine learning algorithms. A total of 805 meteorological records from 2013 to 2015 were collected from government open-source data after preprocessing. In addition to well-known dengue-related factors, we investigated the effects of novel variables, including particulate matter with an aerodynamic diameter < 10 µm (PM10), PM2.5, and an ultraviolet index, for predicting dengue fever occurrence. The collected dataset was randomly divided into an 80% training set and a 20% test set. The experimental results showed that the random forests achieved an area under the receiver operating characteristic curve of 0.9547 for the test set, which was the best compared with the other machine learning algorithms. In addition, the temperature was the most important factor in our variable importance analysis, and it showed a positive effect on dengue fever at < 30 °C but had less of an effect at > 30 °C. The AQIs were not as important as temperature, but one was selected in the process of filtering the variables and showed a certain influence on the final results. CONCLUSIONS: Our study is the first to demonstrate that AQI negatively affects dengue fever occurrence in Taiwan. The proposed prediction model can be used as an early warning system for public health to prevent dengue fever outbreaks.

Optimized machine learning model for air quality index prediction in major cities in India.

Industrial advancements and utilization of large amount of fossil fuels, vehicle pollution, and other calamities increases the Air Quality Index (AQI) of major cities in a drastic manner. Major cities AQI analysis is essential so that the government can take proper preventive, proactive measures to reduce air pollution. This research incorporates artificial intelligence in AQI prediction based on air pollution data. An optimized machine learning model which combines Grey Wolf Optimization (GWO) with the Decision Tree (DT) algorithm for accurate prediction of AQI in major cities of India. Air quality data available in the Kaggle repository is used for experimentation, and major cities like Delhi, Hyderabad, Kolkata, Bangalore, Visakhapatnam, and Chennai are considered for analysis. The proposed model performance is experimentally verified through metrics like R-Square, RMSE, MSE, MAE, and accuracy. Existing machine learning models, like k-nearest Neighbor, Random Forest regressor, and Support vector regressor, are compared with the proposed model. The proposed model attains better prediction performance compared to traditional machine learning algorithms with maximum accuracy of 88.98% for New Delhi city, 91.49% for Bangalore city, 94.48% for Kolkata, 97.66% for Hyderabad, 95.22% for Chennai and 97.68% for Visakhapatnam city.

First temporal distribution model of ambient air pollutants (PM2.5, PM10, and O3) in Yangon City, Myanmar during 2019-2021.

Air pollution has emerged as a significant global concern, particularly in urban centers. This study aims to investigate the temporal distribution of air pollutants, including PM2.5, PM10, and O3, utilizing multiple linear regression modeling. Additionally, the research incorporates the calculation of the Air Quality Index (AQI) and Autoregressive Integrated Moving Average (ARIMA) time series modeling to predict the AQI for PM2.5 and PM10. The concentrations and AQI values for PM2.5 ranged from 0 to 93.6 µg/m3 and 0 to 171, respectively, surpassing the Word Health Organization's (WHO) acceptable threshold levels. Similarly, concentrations and AQI values for PM10 ranged from 0.1 to 149.27 µg/m3 and 2-98 µg/m3, respectively, also exceeding WHO standards. Particulate matter pollution exhibited notable peaks during summer and winter. Key meteorological factors, including dew point temperature, relative humidity, and rainfall, showed a significant negative association with all pollutants, while ambient temperature exhibited a significant positive correlation with particulate matter. Multiple linear regression models of particulate matter for winter season demonstrated the highest model performance, explaining most of the variation in particulate matter concentrations. The annual multiple linear regression model for PM2.5 exhibited the most robust performance, explaining 60% of the variation, while the models for PM10 and O3 explained 45% of the variation in their concentrations. Time series modeling projected an increasing trend in the AQI for particulate matter in 2022. The precise and accurate results of this study serve as a valuable reference for developing effective air pollution control strategies and raising awareness of AQI in Myanmar.

An intelligent interval forecasting system based on fuzzy time series and error distribution characteristics for air quality index.

Due to the emergency environment pollution problems, it is imperative to understand the air quality and take effective measures for environmental governance. As a representative measure, the air quality index (AQI) is a single conceptual index value simplified by the concentrations of several routinely monitored air pollutants according to the proportion of various components in the air. With the gradual enhancement of awareness of environmental protection, air quality index forecasting is a key point of environment management. However, most of the traditional forecasting methods ignore the fuzziness of original data itself and the uncertainty of forecasting results which causes the unsatisfactory results. Thus, an innovative forecasting system combining data preprocessing technique, kernel fuzzy c-means (KFCM) clustering algorithm and fuzzy time series is successfully developed for air quality index forecasting. Concretely, the fuzzy time series that handle the fuzzy set is used for the main forecasting process. Then the complete ensemble empirical mode decomposition and KFCM are respectively developed for data denoising and interval partition. Furthermore, the interval forecasting method based on error distribution is developed to measure the forecasting uncertainty. Finally, the experimental simulation and evaluation system verify the great performance of proposed forecasting system and the promising applicability in a practical environment early warning system.

Online public concern about allergic rhinitis and its association with COVID-19 and air quality in China: an informative epidemiological study using Baidu index.

BACKGROUND: Allergic rhinitis is a common health concern that affects quality of life. This study aims to examine the online search trends of allergic rhinitis in China before and after the COVID-19 epidemic and to explore the association between the daily air quality and online search volumes of allergic rhinitis in Beijing. METHODS: We extracted the online search data of allergic rhinitis-related keywords from the Baidu index database from January 23, 2017 to June 23, 2022. We analyzed and compared the temporal distribution of online search behaviors across different themes of allergic rhinitis before and after the COVID-19 pandemic in mainland China, using the Baidu search index (BSI). We also obtained the air quality index (AQI) data in Beijing and assessed its correlation with daily BSIs of allergic rhinitis. RESULTS: The online search for allergic rhinitis in China showed significant seasonal variations, with two peaks each year in spring from March to May and autumn from August and October. The BSI of total allergic rhinitis-related searches increased gradually from 2017 to 2019, reaching a peak in April 2019, and declined after the COVID-19 pandemic, especially in the first half of 2020. The BSI for all allergic rhinitis themes was significantly lower after the COVID-19 pandemic than before (all p values < 0.05). The results also revealed that, in Beijing, there was a significant negative association between daily BSI and AQI for each allergic rhinitis theme during the original variant strain epidemic period and a significant positive correlation during the Omicron variant period. CONCLUSION: Both air quality and the interventions used for COVID-19 pandemic, including national and local quarantines and mask wearing behaviors, may have affected the incidence and public concern about allergic rhinitis in China. The online search trends can serve as a valuable tool for tracking real-time public concerns about allergic rhinitis. By complementing traditional disease monitoring systems of health departments, these search trends can also offer insights into the patterns of disease outbreaks. Additionally, they can provide references and suggestions regarding the public's knowledge demands related to allergic rhinitis, which can further be instrumental in developing targeted strategies to enhance population-based disease education on allergic diseases.

Enhancing risk communication and environmental crisis management through satellite imagery and AI for air quality index estimation.

Due to climate change, the air pollution problem has become more and more prominent [23]. Air pollution has impacts on people globally, and is considered one of the leading risk factors for premature death worldwide; it was ranked as number 4 according to the website [24]. A study, 'The Global Burden of Disease,' reported 4,506,193 deaths were caused by outdoor air pollution in 2019 [22,25]. The air pollution problem is become even more apparent when it comes to developing countries [22], including Thailand, which is considered one of the developing countries [26]. In this research, we focus and analyze the air pollution in Thailand, which has the annual average PM2.5 (particulate matter 2.5) concentration falls in between 15 and 25, classified as the interim target 2 by 2021's WHO AQG (World Health Organization's Air Quality Guidelines) [27]. (The interim targets refer to areas where the air pollutants concentration is high, with 1 being the highest concentration and decreasing down to 4 [27,28]). However, the methodology proposed here can also be adopted in other areas as well. During the winter in Thailand, Bangkok and its surrounding metroplex have been facing the issue of air pollution (e.g., PM2.5) every year. Currently, air quality measurement is done by simply implementing physical air quality measurement devices at designated-but limited number of locations. In this work, we propose a method that allows us to estimate the Air Quality Index (AQI) on a larger scale by utilizing Landsat 8 images with machine learning techniques. We propose and compare hybrid models with pure regression models to enhance AQI prediction based on satellite images. Our hybrid model consists of two parts as follows:•The classification part and the estimation part, whereas the pure regressor model consists of only one part, which is a pure regression model for AQI estimation.•The two parts of the hybrid model work hand in hand such that the classification part classifies data points into each class of air quality standard, which is then passed to the estimation part to estimate the final AQI. From our experiments, after considering all factors and comparing their performances, we conclude that the hybrid model has a slightly better performance than the pure regressor model, although both models can achieve a generally minimum R2 (R2 > 0.7). We also introduced and tested an additional factor, DOY (day of year), and incorporated it into our model. Additional experiments with similar approaches are also performed and compared. And, the results also show that our hybrid model outperform them. Keywords: climate change, air pollution, air quality assessment, air quality index, AQI, machine learning, AI, Landsat 8, satellite imagery analysis, environmental data analysis, natural disaster monitoring and management, crisis and disaster management and communication.

Association of air quality index (AQI) with incidence of atopic dermatitis in Taiwan: A nationwide population-based cohort study.

BACKGROUND: Air pollutants may aggravate atopic dermatitis (AD). However, the association between Air Quality Index (AQI) and incidence of AD remains unknown. OBJECTIVE: To investigate association between AQI and incidence of AD, using the nationwide cohort in the Taiwan National Health Insurance Research Database (NHIRD). METHODS: We included 21,278,938 participants from the NHIRD not diagnosed with AD before 2008. Long-term average AQI value, obtained from the Taiwan Air Quality Monitoring System Network, before AD diagnosis was calculated and linked for each participant. RESULTS: 199,205 incident cases of AD were identified from 2008 to 2018. Participants were classified into 4 quantiles (Q) by AQI value. With the lowest quantile, Q1, as reference, the AD risk increased significantly in the Q2 group (adjusted hazard ratio [aHR]: 1.29, 95% confidence interval [CI]: 1.04-1.65), Q3 group (aHR: 4.71, 95% CI: 3.78-6.04), and was highest in the Q4 group (aHR: 13.20, 95% CI: 10.86-16.60). As AQI treated as a continuous variable, an increase of 1 unit of AQI value added 7% of AD risk (aHR, 1.07, 95% CI: 1.07-1.08). LIMITATIONS: The NHIRD lacks detailed information on individual subjects. CONCLUSIONS: The results demonstrated a significant positive association between AQI and incidence of AD with a clear dose-response relationship.

Assessment of ambient air quality in relation to the burning of firecrackers during the festival of Diwali: A case study of Jodhpur City (India).

The study attempts to examine the impact of firework activities during Diwali Festival on ambient air quality of Jodhpur city. Air quality parameters particulate matter of diameter 10 µm (PM10), particulate matter of diameter 2.5 µm (PM2.5), sulfur dioxide (SO2), nitrogen dioxide (NO2) and heavy metals in PM2.5 like Pb, Ni, Ba, Al, As and Sr are monitored at two locations, for 15 days, starting from 7 days before the festival of Diwali, on the day of the festival (Diwali) and 7 days after Diwali. On the occasion of Diwali, it was discovered that the 24-h average levels of various pollutants were significantly elevated compared to regular days preceding the festival. Specifically, at the HBO site, the concentrations were notably increased, with sulfur dioxide (SO2) reaching 5.62 times higher, nitrogen dioxide (NO2) at 3 times higher, particulate matter of diameter 10 µm (PM10) at 2.35 times higher, and particulate matter of diameter 2.5 µm (PM2.5) at 1.01 times higher than the usual levels before Diwali. Similarly, at the PTMM site, there were substantial elevations in pollutant concentrations during Diwali compared to pre-festival days, with SO2 registering 2.53 times higher, NO2 at 2.37 times higher, PM2.5 at 1.9 times higher, and PM10 at 1.57 times higher levels than normal. Concentration of Al, Ba, Sr and Pb at HBO site and Al at PTMM site was highest on Diwali day. Air quality index which was in good category on normal days before Diwali, fell into poor category starting from the day before Diwali and remain in poor category on normal days after Diwali. The result indicates the worsening of ambient air quality during Diwali which can adversely impact the human health in terms of various respiratory complications.

Independent and interaction effects of prenatal exposure to high AQI and extreme Humidex on the risk of preterm birth: A large sample population study in northern China.

The combined effects of air pollution and extreme temperature on PTB remain unclear. To evaluate the independent effect and interaction effect of prenatal extreme exposure to air quality index (AQI) and Humidex, on PTB. Based on the National Health Care Data Platform of Shandong University, women who gave birth in 2019-2020 were selected for the study. First, the independent effects of AQI and Humidex on PTB were assessed by logistic regression model. Subsequently, the interaction effects of AQI and Humidex on PTB were estimated separately by calculation of the relative excess risk of interaction (RERI). A total of 34365 pregnant women were included and 1975 subjects were diagnosed with PTB. We observed a significant increase in the odds of PTB associated with maternal high AQI exposure, with an OR of 1.70 (95% CI: 1.59, 1.81). Similarly, extreme exposure to Humidex also demonstrated an elevated PTB odds, with a low Humidex OR of 2.48 (95% CI: 2.23, 2.76) and a high Humidex OR of 1.48 (95% CI: 1.31, 1.67). Finally, we observed an interaction between high AQI and extreme Humidex during the 1st trimester. Interaction effects were noted between high AQI and low Humidex throughout the entire trimester and the 2nd trimester. This study suggests that prenatal exposure to high AQI and extreme Humidex could increase the odds of PTB, with effects exhibiting the sensitivity window and a cumulative trend. Additionally, there is an interaction between AQI and Humidex.

Spatiotemporal empirical analysis of particulate matter PM2.5 pollution and air quality index (AQI) trends in Africa using MERRA-2 reanalysis datasets (1980-2021).

In this study, the spatial-temporal trends of PM2.5 pollution were analyzed for subregions in Africa and the entire continent from 1980 to 2021. The distributions and trends of PM2.5 were derived from the monthly concentrations of the aerosol species from MERRA-2 reanalysis datasets comprising of sulphates (SO4), organic carbon (OC), black carbon (BC), Dust2.5 and Sea Salt (SS2.5). The resulting PM2.5 trends were compared with the climate factors, socio-economic indicators, and terrain characteristics. Using the Mann-Kendall (M-K) test, the continent and its subregions showed positive trends in PM2.5 concentrations, except for western and central Africa which exhibited marginal negative trends. The M-K trends also determined Dust2.5 as the dominant contributing aerosol factor responsible for the high PM2.5 concentrations in the northern, western and central regions of Africa, while SO4 and OC were respectively the most significant contributors to PM2.5 in the eastern and southern Africa regions. For the climate factors, the PM2.5 trends were determined to be positively correlated with the wind speed trends, while precipitation and temperature trends exhibited low and sometimes negative correlations with PM2.5. Socio-economically, highly populated, and bare/sparse vegetated areas showed higher PM2.5 concentrations, while vegetated areas tended to have lower PM2.5 concentrations. Topographically, low laying regions were observed to retain the deposited PM2.5 especially in the northern and western regions of Africa. The Air Quality Index (AQI) results showed that 94 % of the continent had an average PM2.5 of 12-35 µg/m3 hence classified as "Moderate" AQI, and the rest of the continent's PM2.5 levels was between 35 and 55 µg/m3 implying AQI classification of "Unhealthy for Sensitive People". Northern and western Africa regions had the highest AQI, while southern Africa had the lowest AQI. The approach and findings in this study can be used to complement the evaluation and management of air quality in Africa.

The impact of COVID-19 pandemic lockdowns on air quality index: a systematic review.

During the outbreak of the novel coronavirus disease 2019 (COVID-19), many countries implemented lockdown policies to control its transmission. These restrictions provided an opportunity to rest and recover the environment. This systematic review (SR) aimed to evaluate the impact of COVID-19 lockdowns on the Air Quality Index (AQI) in countries worldwide. ScienceDirect and PubMed were searched using relevant keywords to identify studies published until March 2020. Overall, 20 studies were included in the SR based on the eligibility criteria. The results show that COVID-19-related lockdown policies positively affect AQI by restricting air-polluting activities, such as transportation, industry, and construction. However, it is important to note that these policies are ineffective in controlling sources of natural air pollution and local dust. The findings of this study emphasize the need for policymakers to approve legislation limiting the sources of air pollutants.