scholarly journals Prediction of Air Pollutant Concentration Based on One-Dimensional Multi-Scale CNN-LSTM Considering Spatial-Temporal Characteristics: A Case Study of Xi’an, China

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1626
Author(s):  
Hongbin Dai ◽  
Guangqiu Huang ◽  
Jingjing Wang ◽  
Huibin Zeng ◽  
Fangyu Zhou
Keyword(s):  
Air Pollution ◽  
Air Pollutants ◽  
Prediction Models ◽  
Air Pollutant ◽  
Pollutant Concentration ◽  
Concentration Data ◽  
Spatial Characteristics ◽  
Data Prediction ◽  
Temporal And Spatial ◽  
Temporal And Spatial Characteristics

Air pollution has become a serious problem threatening human health. Effective prediction models can help reduce the adverse effects of air pollutants. Accurate predictions of air pollutant concentration can provide a scientific basis for air pollution prevention and control. However, the previous air pollution-related prediction models mainly processed air quality prediction, or the prediction of a single or two air pollutants. Meanwhile, the temporal and spatial characteristics and multiple factors of pollutants were not fully considered. Herein, we establish a deep learning model for an atmospheric pollutant memory network (LSTM) by both applying the one-dimensional multi-scale convolution kernel (ODMSCNN) and a long-short-term memory network (LSTM) on the basis of temporal and spatial characteristics. The temporal and spatial characteristics combine the respective advantages of CNN and LSTM networks. First, ODMSCNN is utilized to extract the temporal and spatial characteristics of air pollutant-related data to form a feature vector, and then the feature vector is input into the LSTM network to predict the concentration of air pollutants. The data set comes from the daily concentration data and hourly concentration data of six atmospheric pollutants (PM2.5, PM10, NO2, CO, O3, SO2) and 17 types of meteorological data in Xi’an. Daily concentration data prediction, hourly concentration data prediction, group data prediction and multi-factor prediction were used to verify the effectiveness of the model. In general, the air pollutant concentration prediction model based on ODMSCNN-LSTM shows a better prediction effect compared with multi-layer perceptron (MLP), CNN, and LSTM models.

scholarly journals Numerical Simulation to Air Pollution Emission Control near an Industrial Zone

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Pravitra Oyjinda ◽  
Nopparat Pochai
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Industrial Development ◽  
Air Pollutant ◽  
Pollutant Concentration ◽  
Air Pollution Control ◽  
Industrial Zone ◽  
Concentration Data ◽  
Monitoring Point ◽  
Concentration Levels

A rapid industrial development causes several environment pollution problems. One of the main problems is air pollution, which affects human health and the environment. The consideration of an air pollutant has to focus on a polluted source. An industrial factory is an important reason that releases the air pollutant into the atmosphere. Thus a mathematical model, an atmospheric diffusion model, is used to estimate air quality that can be used to describe the sulfur dioxide dispersion. In this research, numerical simulations to air pollution measurement near industrial zone are proposed. The air pollution control strategies are simulated to achieve desired pollutant concentration levels. The monitoring points are installed to detect the air pollution concentration data. The numerical experiment of air pollution consisted of different situations such as normal and controlled emissions. The air pollutant concentration is approximated by using an explicit finite difference technique. The solutions of calculated air pollutant concentration in each controlled and uncontrolled point source at the monitoring points are compared. The air pollutant concentration levels for each monitoring point are controlled to be at or below the national air quality standard near industrial zone index.

scholarly journals Association between exposure to ambient air pollution and hospital admission, incidence, and mortality of stroke: an updated systematic review and meta-analysis of more than 23 million participants

2021 ◽  
Vol 26 (1) ◽  
Author(s):  
Zhiping Niu ◽  
Feifei Liu ◽  
Hongmei Yu ◽  
Shaotang Wu ◽  
Hao Xiang
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Hospital Admission ◽  
Air Pollutants ◽  
Meta Analysis ◽  
Air Pollutant ◽  
Pollutant Concentration ◽  
Aerodynamic Diameter ◽  
Stroke Incidence ◽  
Incidence And Mortality

Abstract Background Previous studies have suggested that exposure to air pollution may increase stroke risk, but the results remain inconsistent. Evidence of more recent studies is highly warranted, especially gas air pollutants. Methods We searched PubMed, Embase, and Web of Science to identify studies till February 2020 and conducted a meta-analysis on the association between air pollution (PM2.5, particulate matter with aerodynamic diameter less than 2.5 μm; PM10, particulate matter with aerodynamic diameter less than 10 μm; NO2, nitrogen dioxide; SO2, sulfur dioxide; CO, carbon monoxide; O3, ozone) and stroke (hospital admission, incidence, and mortality). Fixed- or random-effects model was used to calculate pooled odds ratios (OR)/hazard ratio (HR) and their 95% confidence intervals (CI) for a 10 μg/m3 increase in air pollutant concentration. Results A total of 68 studies conducted from more than 23 million participants were included in our meta-analysis. Meta-analyses showed significant associations of all six air pollutants and stroke hospital admission (e.g., PM2.5: OR = 1.008 (95% CI 1.005, 1.011); NO2: OR = 1.023 (95% CI 1.015, 1.030), per 10 μg/m3 increases in air pollutant concentration). Exposure to PM2.5, SO2, and NO2 was associated with increased risks of stroke incidence (PM2.5: HR = 1.048 (95% CI 1.020, 1.076); SO2: HR = 1.002 (95% CI 1.000, 1.003); NO2: HR = 1.002 (95% CI 1.000, 1.003), respectively). However, no significant differences were found in associations of PM10, CO, O3, and stroke incidence. Except for CO and O3, we found that higher level of air pollution (PM2.5, PM10, SO2, and NO2) exposure was associated with higher stroke mortality (e.g., PM10: OR = 1.006 (95% CI 1.003, 1.010), SO2: OR = 1.006 (95% CI 1.005, 1.008). Conclusions Exposure to air pollution was positively associated with an increased risk of stroke hospital admission (PM2.5, PM10, SO2, NO2, CO, and O3), incidence (PM2.5, SO2, and NO2), and mortality (PM2.5, PM10, SO2, and NO2). Our study would provide a more comprehensive evidence of air pollution and stroke, especially SO2 and NO2.

scholarly journals Seasonal Patterns and Trends of Air Pollution in The Upper Northern Thailand from 2004 to 2018

2021 ◽  
Author(s):  
Sarawut Sukkhum ◽  
Apiradee Lim ◽  
Rattikan Saelim ◽  
Thammasin Ingviya
Keyword(s):  
Air Pollution ◽  
Pollution Control ◽  
Air Pollutants ◽  
Air Pollutant ◽  
Seasonal Patterns ◽  
Average Increase ◽  
Concentration Data ◽  
Northern Thailand ◽  
The Past ◽  
Control Department

Abstract The objective of this study was to investigate the seasonal patterns and trends of air pollutants in upper northern Thailand (UNT) from 2004 to 2018. The hourly air pollutant concentration data recorded from 6 monitoring stations in the UNT were obtained from the Pollution Control Department, Ministry of Natural Resources and Environment of Thailand. Cubic splines were used to assess seasonal patterns and trends of air pollutants. Linear regression was used to estimate the average increase in concentrations of air pollutants at each monitoring station. The results exhibited seasonal patterns for CO, NOX, NO2, O3, and PM10, in all stations while SO2 only in one station in Lampang and all stations in Chiangmai. The concentrations of these pollutants rose during August and September and reached peak levels between March and April. In the past 15 years, the levels of overall CO, O3, and SO2 in the UNT had significantly increased, on average of 0.032 ppm, 0.012 ppb, and 0.017 ppb, respectively. In contrast, NO2, NOX, and PM10 had significantly decreased on average of 0.012 ppb, 0.011 ppb, and 0.016 mg/m3, respectively. In conclusion, it should be of concern for such activities that related to air pollutants variation accordingly.

scholarly journals Relationship between Air Pollutant Exposure and Gynecologic Cancer Risk

2021 ◽  
Vol 18 (10) ◽  
pp. 5353
Author(s):  
Qiwei Yu ◽  
Liqiang Zhang ◽  
Kun Hou ◽  
Jingwen Li ◽  
Suhong Liu ◽  
...  
Keyword(s):  
Air Pollution ◽  
Cancer Risk ◽  
Air Pollutants ◽  
Gynecological Cancer ◽  
Gynecologic Cancer ◽  
Ambient Air ◽  
Air Pollutant ◽  
Ambient Air Pollution ◽  
Short Term Exposure ◽  
Increased Risk

Exposure to air pollution has been suggested to be associated with an increased risk of women’s health disorders. However, it remains unknown to what extent changes in ambient air pollution affect gynecological cancer. In our case–control study, the logistic regression model was combined with the restricted cubic spline to examine the association of short-term exposure to air pollution with gynecological cancer events using the clinical data of 35,989 women in Beijing from December 2008 to December 2017. We assessed the women’s exposure to air pollutants using the monitor located nearest to each woman’s residence and working places, adjusting for age, occupation, ambient temperature, and ambient humidity. The adjusted odds ratios (ORs) were examined to evaluate gynecologic cancer risk in six time windows (Phase 1–Phase 6) of women’s exposure to air pollutants (PM2.5, CO, O3, and SO2) and the highest ORs were found in Phase 4 (240 days). Then, the higher adjusted ORs were found associated with the increased concentrations of each pollutant (PM2.5, CO, O3, and SO2) in Phase 4. For instance, the adjusted OR of gynecological cancer risk for a 1.0-mg m−3 increase in CO exposures was 1.010 (95% CI: 0.881–1.139) below 0.8 mg m−3, 1.032 (95% CI: 0.871–1.194) at 0.8–1.0 mg m−3, 1.059 (95% CI: 0.973–1.145) at 1.0–1.4 mg m−3, and 1.120 (95% CI: 0.993–1.246) above 1.4 mg m−3. The ORs calculated in different air pollution levels accessed us to identify the nonlinear association between women’s exposure to air pollutants (PM2.5, CO, O3, and SO2) and the gynecological cancer risk. This study supports that the gynecologic risks associated with air pollution should be considered in improved public health preventive measures and policymaking to minimize the dangerous effects of air pollution.

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