Explore the vast range of titles available.

Particulate matter and air pollution in Korea are becoming worse. There is a lack of research regarding the impact of particulate matter on patients with COPD. Therefore, the purpose of this study was to investigate the effects of various air pollution factors, including particulate matter, on the incidence rate of severe acute exacerbations of COPD (AECOPD) events. We analyzed the relationship between air pollutants and AECOPD events that required hospitalization at Guro Hospital in Korea from January 1, 2015 to May 31, 2017. We used general linear models with Poisson distribution and log-transformation to obtain adjusted relative risk (RR). We conducted further analysis through the Comprehensive Air-quality Index (CAI) that is used in Korea. Among various other air pollutants, particulate matter was identified as a major source of air pollution in Korea. When the CAI score was over 50, the incidence rate of severe AECOPD events was statistically significantly higher [RR 1.612, 95% CI, 1.065-2.440, =0.024]. Additionally, the particulate matter levels 3 days before hospitalization were statistically significant [RR 1.003, 95% CI, 1.001-1.005, =0.006]. Particulate matter and air pollution increase the incidence rate of severe AECOPD events. COPD patients should be cautioned against outdoor activities when particulate matter levels are high.

The air quality index has a severe effect on the determination of health conditions of a city. The prediction of air quality index can aid in determining the optimum route in case of traffic and it can also aid in determining the pollutants which have severe impact on human health conditions. The paper presents an air quality prediction system using various machine learning based models. The air quality index is determined by measuring the different gases present in the atmosphere. In this paper we have considered seven such parameters as concentration levels of Particulate Matter 2.5 (PM2.5), Particulate Matter 10 (PM10), Carbon Mono oxide (CO), Nitrogen Dioxide (NO2), Ammonia (NH3), Sulphur Dioxide (SO2) and Ozone (O3) levels for the duration between the year January 2019 to October 2023 for a crowded area of Varanasi city. The various pre processing techniques have been used in the dataset for the implementation of machine learning models. The performance of the models have been compared for the prediction of the air quality. The results show that the Random Forest and Decision Tree based model achieves the maximum accuracy of approximately 100% as compared to 98%, 95% and 93% and 79% for the SVM, Multi layer Perceptron network, KNN classification and Linear Regression.

Air pollution poses a significant threat to the health of the environment and human well-being. The air quality index (AQI) is an important measure of air pollution that describes the degree of air pollution and its impact on health. Therefore, accurate and reliable prediction of the AQI is critical but challenging due to the non-linearity and stochastic nature of air particles. This research aims to propose an AQI prediction hybrid deep learning model based on the Attention Convolutional Neural Networks (ACNN), Autoregressive Integrated Moving Average (ARIMA), Quantum Particle Swarm Optimization (QPSO)-enhanced-Long Short-Term Memory (LSTM) and XGBoost modelling techniques. Daily air quality data were collected from the official Seoul Air registry for the period 2021 to 2022. The data were first preprocessed through the ARIMA model to capture and fit the linear part of the data and followed by a hybrid deep learning architecture developed in the pretraining–finetuning framework for the non-linear part of the data. This hybrid model first used convolution to extract the deep features of the original air quality data, and then used the QPSO to optimize the hyperparameter for LSTM network for mining the long-terms time series features, and the XGBoost model was adopted to fine-tune the final AQI prediction model. The robustness and reliability of the resulting model were assessed and compared with other widely used models and across meteorological stations. Our proposed model achieves up to 31.13% reduction in MSE, 19.03% reduction in MAE and 2% improvement in R-squared compared to the best appropriate conventional model, indicating a much stronger magnitude of relationships between predicted and actual values. The overall results show that the attentive hybrid deep Quantum inspired Particle Swarm Optimization model is more feasible and efficient in predicting air quality index at both city-wide and station-specific levels.

The measurement of air quality parameters for indoor environments is of increasing importance to provide sufficient safety conditions for workers, especially in places including dangerous chemicals and materials such as laboratories, factories, and industrial locations. Indoor air quality index (IAQ-index) and total volatile organic Compounds (TVOC) are two important parameters to measure air impurities or air pollution. Both parameters are widely used in gases sensing applications. In this paper, the IAQ-index and TVOCs have been investigated to identify the best and most flexible solution for air quality threshold selection of hazardous/toxic gases detection and alarming systems. The TVOCs from the SGP30 gas sensor and the IAQ-index from the SGP40 gas sensor were tested with 12 different organic solvents. The two gas sensors are combined with an IoT-based microcontroller for data acquisition and data transfer to an IoT-cloud for further processing, storing, and monitoring purposes. Extensive tests of both sensors were carried out to determine the minimum detectable volume depending on the distance between the sensor node and the leakage source. The test scenarios included static tests in a classical chemical hood, as well as tests with a mobile robot in an automated sample preparation laboratory with different positions.

The emergence of low-cost air quality sensors may improve our ability to capture variations in urban air pollution and provide actionable information for public health. Despite the increasing popularity of low-cost sensors, there remain some gaps in the understanding of their performance under real-world conditions, as well as compared to regulatory monitors with high accuracy, but also high cost and maintenance requirements. In this paper, we report on the performance and the linear calibration of readings from 12 commercial low-cost sensors co-located at a regulatory air quality monitoring site in Dallas, Texas, for 18 continuous measurement months. Commercial AQY1 sensors were used, and their reported readings of O3, NO2, PM2.5, and PM10 were assessed against a regulatory monitor. We assessed how well the raw and calibrated AQY1 readings matched the regulatory monitor and whether meteorology impacted performance. We found that each sensor’s response was different. Overall, the sensors performed best for O3 (R2 = 0.36–0.97) and worst for NO2 (0.00–0.58), showing a potential impact of meteorological factors, with an effect of temperature on O3 and relative humidity on PM. Calibration seemed to improve the accuracy, but not in all cases or for all performance metrics (e.g., precision versus bias), and it was limited to a linear calibration in this study. Our data showed that it is critical for users to regularly calibrate low-cost sensors and monitor data once they are installed, as sensors may not be operating properly, which may result in the loss of large amounts of data. We also recommend that co-location should be as exact as possible, minimizing the distance between sensors and regulatory monitors, and that the sampling orientation is similar. There were important deviations between the AQY1 and regulatory monitors’ readings, which in small part depended on meteorology, hindering the ability of the low-costs sensors to present air quality accurately. However, categorizing air pollution levels, using for example the Air Quality Index framework, rather than reporting absolute readings, may be a more suitable approach. In addition, more sophisticated calibration methods, including accounting for individual sensor performance, may further improve performance. This work adds to the literature by assessing the performance of low-cost sensors over one of the longest durations reported to date.

Background The Mexico City Metropolitan Area has an expansive urban population and a long history of air quality management challenges. Poor air quality has been associated with adverse pulmonary and cardiac health effects, particularly among susceptible populations with underlying disease. In addition to reducing pollution concentrations, risk communication efforts that inform behavior modification have the potential to reduce public health burdens associated with air pollution. Methods This study investigates the utilization of Mexico’s IMECA risk communication index to inform air pollution avoidance behavior among the general population living in the Mexico City Metropolitan Area. Individuals were selected via probability sampling and surveyed by phone about their air quality index knowledge, pollution concerns, and individual behaviors. Results The results indicated reasonably high awareness of the air quality index (53% of respondents), with greater awareness in urban areas, among older and more educated individuals, and for those who received air quality information from a healthcare provider. Additionally, behavior modification was less influenced by index reports as it was by personal perceptions of air quality, and there was no difference in behavior modification among susceptible and non-susceptible groups. Conclusions Taken together, these results suggest there are opportunities to improve the public health impact of risk communication through an increased focus on susceptible populations and greater encouragement of public action in response to local air quality indices.

Air pollution is a required environmental and public health issue in India, with multiple municipalities repeatedly ranking among the most polluted in the world. This study leverages large datasets to construct a predictive model for forecasting air quality trends using a novel approach that integrates the Recency Frequency Monetary (RFM) model with deep learning. The research aims to efficiently quantify pollution events frequency and assess the impact of air quality variations on public health, offering a more flexible and adaptive system for air quality monitoring. As a result, a large volume of air quality data provided by RFM (Recency, Frequency, and Monetary) will be flexible and frequently handled and analyzed. In this research, the performance of the integrated RFM technology is examined using Python and Google Colab, and the simulation results are compared to air pollution information from neural networks for structures in additional data using existing air quality monitoring systems in India. Performance examination of both regression and classification techniques in RFM. The execution of RFM can be one of the models and its potential to enhance air quality monitoring and urban sustainability

This paper forecasts the hourly AQI in Azamgarh, Uttar Pradesh, India, using machine learning (ML) models. This work involved the real-time measurement of hourly particulate matter (PM 2.5 , PM 10 ), gaseous concentrations (NO 2 , SO 2 ), and meteorological parameters (temperature, relative humidity, wind direction, wind speed, and uv radiation) in the ambient air. We have calculated the pollutant subindex and hourly AQI for Azamgarh from July 2022 to June 2023, encompassing a total of 8760 data points. The study observed annual average concentrations of NO 2 (33.91 ± 30.38 µg/m 3 ), SO 2 (43.96 ± 20.12 µg/m 3 ), PM 2.5 (52.25 ± 55.26 µg/m 3 ), and PM 10 (77.58 ± 80.61 µg/m 3 ). Meteorological conditions included a mean temperature of 27.20 ± 7.65 °C, relative humidity of 63.93 ± 18.74%, wind speed of 1.00 ± 0.96 m/s, and a uv index of 0.42 ± 0.34. The computed annual mean hourly AQI was 123 ± 91.96, which falls into the moderate-level pollution category. The AQI values in winter were higher than in summer. ANOVA analysis shows statistically significant monthly changes in pollutants and meteorological parameters: PM 2.5 (F = 842.7), PM 10 (F = 773.5), NO 2 (F = 583.6), SO 2 (F = 3356), temperature (F = 1045), humidity (F = 572.5), wind speed (F = 36.9), wind direction (F = 54.27), uv index (F = 24.48), and hourly AQI (F = 738.4), all with p < 2e-16. We predicted the hourly AQI using eight machine learning models: adaboost, catboost, gradient boosting, knn, linear regression, random forest, svm, and xgboost. With the fastest computational time of 1.61 s out of all the models examined, xgboost stood out as the best model because of its outstanding R 2 and RMSE performances. We employed a tenfold cross-validation to evaluate the models' performance. The results of the paired t-test indicate that catboost, gradboost, linear regression, and xgboost statistically align closely with the observed data. We can also observe the models' comparative performance using a taylor diagram. An investigation of sensitivity revealed that PM 2.5 , PM 10 , NO 2 , and SO 2 are crucial for the accuracy of the xgboost model in predicting AQI. Excluding these pollutants greatly reduces performance. In conclusion, machine learning can predict AQI, and xgboost is the best model for this purpose. Graphical Abstract Highlights Hourly gaseous, particulate and metrological components were measured (8760 data points). To forecast hourly AQI, we employed eight machine-learning algorithms. Annual mean AQI is 123 (moderate); higher in winter. XGboost stood out as the best models because of their outstanding R-squared, RMSE and least computational time (1.61 s). Cross-validation (tenfold) and Taylor diagram was employed to evaluate the models' performance. Sensitivity analysis reveals PM 2.5 , PM 10 , NO 2 , and SO 2 are crucial for XGboost's AQI predictions.

Background Maternal exposure to air pollution is related to fetal dysplasia. However, the association between maternal exposure to air pollution and the risk of congenital hypothyroidism (CH) in the offspring is largely unknown. Methods We conducted a national database based study in China to explore the association between these two parameters. The incidence of CH was collected from October 1, 2014 to October 1, 2015 from the Chinese Maternal and Child Health Surveillance Network. Considering that total period of pregnancy and consequently the total period of particle exposure is approximately 10 months, average exposure levels of PM 2.5 , PM 10 and Air Quality Index (AQI) were collected from January 1, 2014 to January 1, 2015. Generalized additive model was used to evaluate the association between air pollution and the incidence of CH, and constructing receiver operating characteristic (ROC) curve was used to calculate the cut-off value. Results The overall incidence of CH was 4.31 per 10,000 screened newborns in China from October 1, 2014 to October 1, 2015. For every increase of 1 μg/m 3 in the PM 2.5 exposure during gestation could increase the risk of CH (adjusted OR = 1.016 per 1 μg/m 3 change, 95% CI, 1.001–1.031). But no significant associations were found with regard to PM 10 (adjusted OR = 1.009, 95% CI, 0.996–1.018) or AQI (adjusted OR = 1.012, 95% CI,0.998–1.026) and the risk of CH in the offspring. The cut-off value of prenatal PM 2.5 exposure for predicting the risk of CH in the offspring was 61.165 μg/m 3 . Conclusions The present study suggested that maternal exposure to PM 2.5 may exhibit a positive association with increased risk of CH in the offspring. We also proposed a cut-off value of PM 2.5 exposure that might determine reduction in the risk of CH in the offspring in highly polluted areas.

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 (PM 10 ), PM 2.5 , 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.