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.

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.

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.

Association between Air Quality Index and the risk of rosacea: A Nationwide population-based Cohort Study.

BACKGROUND: Air pollution is associated with several inflammatory skin disorders. However, the association between air quality and rosacea remains unclear. OBJECTIVE: To investigate the association between air quality index and incidence of rosacea. METHODS: Overall, 21,709,479 participants without rosacea before 2008 were recruited from the Taiwan National Health Insurance Research Database. The long-term average air quality index (AQI) value for each participant was acquired from the Taiwan Air Quality Monitoring System Network and calculated from 2008/1/1 until the diagnosis of rosacea, withdrawal from the National Health Insurance, or December 31, 2018. RESULTS: We observed a significant association between AQI and the incidence of rosacea, with each unit elevation in AQI increasing the risk of rosacea by 5 %. Compared with the Q1 group, the Q2, Q3, and Q4 cohorts exhibited 1.82-fold, 4.48-fold and 7.22-fold increased risk of rosacea, respectively. Additionally, exposure to PM2.5, SO2 and CO increased the risk of rosacea, whereas exposure to PM10 was associated with a lower risk. CONCLUSION: This study supported a significant dose-response relationship between AQI and the incidence of rosacea.

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.

Negative association of atmospheric pollutants with semen quality: A cross-sectional study in Taiyuan, China.

BACKGROUND: In recent decades, the quality of male semen has decreased worldwide. Air pollution has been linked to lower semen quality in several studies. However, the effects of atmospheric pollutants on different semen characteristics have not always been consistent. The aim of this study was to investigate the association between the Air Quality Index (AQI) and six atmospheric pollutants (PM2.5, PM10, SO2, NO2, CO, and O3), semen quality, and their key exposure window periods. METHODS: This study included 1711 semen samples collected at the reproductive clinics of the First Affiliated Hospital of Shanxi Medical University in Taiyuan, Shanxi, China, from October 10, 2021, to September 30, 2022. We evaluated the association of AQI and six atmospheric pollutants with semen quality parameters throughout sperm development and three key exposure windows in men using single-pollutant models, double-pollutant models, and subgroup analyses of semen quality-eligible groups. RESULTS: Both the single-pollutant model and subgroup analyses showed that PM, CO, and O3 levels were negatively correlated with total and progressive motility. At 70-90 d before semen collection, CO exposure and semen volume (ß =-1.341, 95 % CI: -1.805, -0.877, P <0.001), total motility (ß =-2.593, 95 % CI: -3.425, -1.761, P <0.001), and progressive motility (ß =-4.658, 95 % CI: -5.556, -3.760, P <0.001) were negatively correlated. At 0-9 days before semen collection, CO was negatively correlated with normal morphology (ß =-3.403, 95 % CI: -5.099, -1.708, P <0.001). Additionally, the AQI was adversely associated with total and progressive motility in subgroup analyses of the semen quality-eligible groups. CONCLUSIONS: During the entire sperm development process, multiple air pollutants were determined to have an adverse correlation with semen quality parameters. AQI was significant marker for the combined effects of various atmospheric pollutants on male reproductive health.

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.

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.

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.

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.

Performance analysis of machine learning models for AQI prediction in Gorakhpur City: a critical study.

Air pollution and climate change are two complementary forces that directly or indirectly affect the environment's physical, chemical, and biological processes. The air quality index is a parameter defined to cope with this effect of air pollution. This study delves deeper into predicting this AQI parameter using multiple machine learning-based models. The AQI pollutants considered for this study are particulate matter (PM10, PM2.5), SO2, and NO2. It also tries to develop a comparative analysis of two different machine learning (ML) models viz. a viz. XGBoost and Lasso regression. An ever-changing emission concentration of pollutants is displayed by this study conducted in the urban city of Gorakhpur Uttar Pradesh, India. The validation of prediction accuracies of models was done over several statistical metrics. The value of the R2 metric for XGBoost (0.9985) is comparatively more than the R2 value for Lasso regression (0.9218) indicating lesser variance and higher accuracy of XGBoost in predicting AQI. Various statistical measures are taken into consideration in this study, including mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), T-test and p-values, and confidence intervals (CI). An increased degree of model accuracy is suggested as XGBoost's MAE, MSE, and RMSE values are significantly lower than Lasso's. Statistically significant performance differences between the XGBoost and Lasso regression models are demonstrated by T-statistics and p-values for MAE, MSE, RMSE, and R2.

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.

Enhanced Air Quality Prediction Using a Coupled DVMD Informer-CNN-LSTM Model Optimized with Dung Beetle Algorithm.

Accurate prediction of air quality is crucial for assessing the state of the atmospheric environment, especially considering the nonlinearity, volatility, and abrupt changes in air quality data. This paper introduces an air quality index (AQI) prediction model based on the Dung Beetle Algorithm (DBO) aimed at overcoming limitations in traditional prediction models, such as inadequate access to data features, challenges in parameter setting, and accuracy constraints. The proposed model optimizes the parameters of Variational Mode Decomposition (VMD) and integrates the Informer adaptive sequential prediction model with the Convolutional Neural Network-Long Short Term Memory (CNN-LSTM). Initially, the correlation coefficient method is utilized to identify key impact features from multivariate weather and meteorological data. Subsequently, penalty factors and the number of variational modes in the VMD are optimized using DBO. The optimized parameters are utilized to develop a variationally constrained model to decompose the air quality sequence. The data are categorized based on approximate entropy, and high-frequency data are fed into the Informer model, while low-frequency data are fed into the CNN-LSTM model. The predicted values of the subsystems are then combined and reconstructed to obtain the AQI prediction results. Evaluation using actual monitoring data from Beijing demonstrates that the proposed coupling prediction model of the air quality index in this paper is superior to other parameter optimization models. The Mean Absolute Error (MAE) decreases by 13.59%, the Root-Mean-Square Error (RMSE) decreases by 7.04%, and the R-square (R2) increases by 1.39%. This model surpasses 11 other models in terms of lower error rates and enhances prediction accuracy. Compared with the mainstream swarm intelligence optimization algorithm, DBO, as an optimization algorithm, demonstrates higher computational efficiency and is closer to the actual value. The proposed coupling model provides a new method for air quality index prediction.

Impact of air pollutants on climate change and prediction of air quality index using machine learning models.

The impact of air pollution in Chennai metropolitan city, a southern Indian coastal city was examined to predict the Air Quality Index (AQI). Regular monitoring and prediction of the Air Quality Index (AQI) are critical for combating air pollution. The current study created machine learning models such as XGBoost, Random Forest, BaggingRegressor, and LGBMRegressor for the prediction of the AQI using the historical data available from 2017 to 2022. According to historical data, the AQI is highest in January, with a mean value of 104.6 g/gm, and the lowest in August, with a mean AQI value of 63.87 g/gm. Particulate matter, gaseous pollutants, and meteorological parameters were used to predict AQI, and the heat map generated showed that of all the parameters, PM2.5 has the greatest impact on AQI, with a value of 0.91. The log transformation method is used to normalize datasets and determine skewness and kurtosis. The XGBoost model demonstrated strong performance, achieving an R2 (correlation coefficient) of 0.9935, a mean absolute error (MAE) of 0.02, a mean square error (MSE) of 0.001, and a root mean square error (RMSE) of 0.04. In comparison, the LightGBM model's prediction was less effective, as it attained an R2 of 0.9748. According to the study, the AQI in Chennai has been increasing over the last two years, and if the same conditions persist, the city's air pollution will worsen in the future. Furthermore, accurate future air quality level predictions can be made using historical data and advanced machine learning algorithms.

Long-Range Network of Air Quality Index Sensors in an Urban Area.

In recent times the escalating pollution within densely populated metropolitan areas has emerged as a significant and pressing concern. Authorities are actively grappling with the challenge of devising solutions to promote a cleaner and more environmentally friendly urban landscapes. This paper outlines the potential of establishing a LoRa node network within a densely populated urban environment. Each LoRa node in this network is equipped with an air quality measurement sensor. This interconnected system efficiently transmits all the analyzed data to a gateway, which subsequently sends it to a server or database in real time. These data are then harnessed to create a pollution map for the corresponding area, providing users with the opportunity to assess local pollution levels and their recent variations. Furthermore, this information proves valuable when determining the optimal route between two points in the city, enabling users to select the path with the lowest pollution levels, thus enhancing the overall quality of the urban environment. This advantage contributes to alleviating congestion and reducing excessive pollution often concentrated behind buildings or on adjacent streets.

Predictive modeling and analysis of air quality - Visualizing before and during COVID-19 scenarios.

Quality air to breathe is the basic necessity for an individual and in recent times, emission from various sources caused by human activities has resulted in substantial degradation in the air quality. This work focuses to study the inadvertent effect of COVID-19 lockdown on air pollution. Pollutants' concentration before- and during- COVID-19 lockdown is captured to understand the variation in air quality. Firstly, multi-pollutant profiling using hierarchical cluster analysis of pollutants' concentration is performed that highlights the differences in the cluster compositions between before- and during-lockdown time periods. Results show that the particulate matter (PM10 and PM2.5) in air that formed the primary cluster before lock-down, came down to close similarity with other clusters during lockdown. Secondly, predicting air quality index (AQI) based on the forecasts of pollutants' concentration is performed using neural networks, support vector machine, decision tree, random forest, and boosting algorithms. The best-fitted models representing AQI is identified separately for before- and during-lockdown time periods based on its predictive power. While deterministic method reactively evaluates present AQI when current pollutants' concentration at a particular time and place are known, this study uses the best fitted data-driven model to determine future AQIs based on the forecasts of pollutant's concentration accurately (overall RMSE<0.1 for before lockdown scenario and <0.3 for during lockdown scenario). The study contributes to visualize the variation in pollutants' concentrations between the two scenarios. The results show that the reduced economic activities during lockdown period had led to the drop in concentration of PM10 and PM2.5 by 27% and 50% on an average. The findings of this study have practical and societal implications and serve as a reference mechanism for policymakers and governing bodies to revise their actions plans for regulating individual air pollutants in the atmospheric air.

AQI prediction using layer recurrent neural network model: a new approach.

The air quality index (AQI) prediction is important to evaluate the effects of air pollutants on human health. The airborne pollutants have been a major threat in Delhi both in the past and coming years. The air quality index is a figure, based on the cumulative effect of major air pollutant concentrations, used by Government agencies, for air quality assessment. Thus, the main aim of the present study is to predict the daily AQI one year in advance through three different neural network models (FF-NN, CF-NN and LR-NN) for the year 2020 and compare them. The models were trained using AQI values of previous year (2019). In addition to main air pollutants like PM10/PM2.5, O3, SO2, NOx, CO and NH3, the non-criteria pollutants and meteorological data were also included as input parameter in this study. The model performances were assessed using statistical analysis. The key air pollutants contributing to high level of daily AQI were found to be PM2.5/PM10, CO and NO2. The root mean square error (RMSE) values of 31.86 and 28.03 were obtained for the FF-NN and CF-NN models respectively whereas the LR-NN model has the minimum RMSE value of 26.79. LR-NN algorithm predicted the AQI values very closely to the actual values in almost all the seasons of the year. The LR-NN performance was also found to be the best in post-monsoon season i.e., October and November (maximum R2 = 0.94) with respect to other seasons. The study would aid air pollution control authorities to predict AQI more precisely and adopt suitable pollution control measures. Further research studies are recommended to compare the performance of LR-NN model with statistical, numerical and computational models for accurate air quality assessment.

How does the COVID-19-related restriction affect the spatiotemporal variability of ambient air quality in a tropical city?

The ambient air, a significant hazard to human health in most Indian cities, including Rourkela, is something we are strangely neglecting in the age of industrialization and urbanization. High levels of particulate matter released from various anthropogenic sources over the past decade have had a significant negative impact on the city. The COVID-19 lockdown situation brings understanding and realization towards the improvement of air quality and its subsequent effects. The present study investigates the impact of the COVID-19-related lockdown on the spatiotemporal variation of the ambient air quality in Rourkela City with a tropical climatic setup. The concentration and distribution of various pollutants are well explained by the wind rose and Pearson correlation. There is considerable spatiotemporal variation in the city's ambient air quality, as determined by a two-way ANOVA test comparing sampling sites and months. During the COVID-19 lockdown phases, the air quality of Rourkela witnessed an improvement in annual AQI ranging from 12.64 to 26.85% across the city. However, the air quality in the city deteriorated by 13.76-65.79% after the revocation of COVID-19 restrictions. The paired sample T-test justified that the air quality of Rourkela was significantly healthier in 2020 compared to both 2019 and 2021. Spatial interpolation reveals that the ambient air quality of Rourkela ranged from satisfactory to moderate categories throughout the entire study period. 31.93% area of the city has experienced an improvement in AQI from the Moderate to the satisfying category from 2019 to 2020, whereas about 68.78% area of the city has witnessed a decline in AQI from satisfactory to moderate category from 2020 to 2021.

Measurement of fine particle concentrations and estimation of air quality index (AQI) over northeast Douala, Cameroon.

Due to absence of data on air quality monitoring and pollutant emissions in Douala, a measurement campaign along the principal street passage to the college grounds was started. Using the OC 300 Laser Dust Particle, fine particle concentrations are monitored during 1 week from Monday to Sunday. The instrument used detects four different sizes of particles: PM10, PM5, PM2.5, and PM1. The daily average concentrations measured ranged from 9.47 ± 0.26 to 50.14 ± 2.42 µg·m-3 for PM1.0; 13.13 ± 0.38 to 86.65 ± 3.96 µg·m-3 for PM2.5; 13.60 ± 0.40 to 100.56 ± 4.20 µg·m-3 for PM5; and 14.52 ± 0.42 to 114.59 ± 4.60 µg·m-3 for PM10. Exceptions made from PM5 and PM1.0 which were not in relation to the WHO (World Health Organization) guideline values, the level of PM10 and PM2.5 is higher than the WHO standards. The air quality index (AQI) is between very poor and poor during this measurement campaign, indicating that residents of the study region are highly exposed. Through the use of correlation studies, it has been demonstrated that the predominant source of fine particles in the studied region is vehicular activity. As a result, traffic density is the most significant factor causing the different air pollution levels seen in the tested areas.

Air mapping during COVID-19 and association between air pollutants and physiochemical parameters of the plants using structural equal modeling: a case study.

In urban areas around the world, air pollution introduced by vehicular movement is a key concern. However, restricting vehicular traffic during the COVID-19 shutdown improved air quality to some extent. This study was conducted out in the smart city of Bhubaneswar, which is also the state capital of Odisha, India. The study has tried to map Bhubaneswar by collecting the air quality data before, during, and after the COVID lockdown of six air quality monitoring stations present in Bhubaneswar established under "National Ambient Air Monitoring Program" (NAMP). Furthermore, plants, which are the most vulnerable to air pollution, can show a variety of visible changes depending on their level of sensitivity. Moreover, leaves of Mangifera indica, Monoon longifolium, Azadirachta indica, Millettia pinnata, Aegle marmelos were collected from nearby of six air monitoring stations to assess the "Air Pollution Tolerance Index." M. indica was found to be intermediately tolerant, and all of the other species were found to be sensitive. The structural equation modeling results also revealed a significant relationship between total chlorophyll content, relative water content, ascorbic acid content, leaf extract pH, APTI with species, air quality index, and PM10.