Spatiotemporal Dynamics and Exposure Analysis of Daily PM2.5 Using a Remote Sensing-based Machine Learning Model and Multi-time Meteorological Parameters

2020 ◽  
Author(s):  
Binjie Chen ◽  
Yi Lin ◽  
Jinsong Deng ◽  
Zheyu Li ◽  
Li Dong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Meteorological Parameters ◽  
Fine Particulate Matter ◽  
Model Performance ◽  
World Health ◽  
Exposure Level ◽  
Human Beings ◽  
Exposure Analysis ◽  
The Yrd

Abstract Background Identifying spatiotemporal characteristics of daily fine particulate matter (PM2.5) concentrations is essential for assessing air quality. Exposure analysis can help understand the environmental health impact on human beings and provide basic information for appropriate decision making. This study aimed to estimate daily PM2.5 concentrations and analyze the resident exposure level in the economically developed Yangtze River Delta (YRD) from 2016–2018. Methods An integrated method incorporating satellite-based aerosol optical depth (AOD), machine learning models and multi-time meteorological parameters were developed. Ten-fold cross validation (CV) was implemented to evaluate the model performance. Results Compared to the models with daily means of meteorological fields, the models with multi-time meteorological parameters had higher CV R2 and lower CV root mean square error (RMSE) values. The model with the best performance achieved sample- (site-) based CV R2 values of 0.88 (0.88) and RMSE values of 10.33 (10.35) µg/m3. The YRD region is seriously polluted (exceeding the World Health Organization (WHO) Interim Targets (IT)-1 standard of 35 µg/m3) during our study period, especially in Jiangsu Province, but with an improving trend. The residents in Zhejiang Province suffered the least from exposure, with 39 days (4% of the total days) characterized as over polluted (daily average > 75 µg/m3) in our study period. Air pollution in Shanghai Municipality mitigated the most from 2016 to 2018. Conclusions With the advantages of high-accuracy and high-resolution (daily and 0.01°×0.01° resolutions), the proposed method can help explore the effect of air pollution to human health spatiotemporally and guide for environmental policy planning.

scholarly journals Comparison of Liquefied Petroleum Gas Cookstoves and Wood Cooking Fires on PM2.5 Trends in Brick Workers’ Homes in Nepal

2020 ◽  
Vol 17 (16) ◽  
pp. 5681
Author(s):  
James D. Johnston ◽  
Megan E. Hawks ◽  
Haley B. Johnston ◽  
Laurel A. Johnson ◽  
John D. Beard
Keyword(s):  
Air Pollution ◽  
Geometric Mean ◽  
Fine Particulate Matter ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
World Health ◽  
Liquefied Petroleum Gas ◽  
High Prevalence ◽  
Health Organization ◽  
Indoor And Outdoor

Prior studies document a high prevalence of respiratory symptoms among brick workers in Nepal, which may be partially caused by non-occupational exposure to fine particulate matter (PM2.5) from cooking. In this study, we compared PM2.5 levels and 24 h trends in brick workers’ homes that used wood or liquefied petroleum gas (LPG) cooking fuel. PM2.5 filter-based and real-time nephelometer data were collected for approximately 24 h in homes and outdoors. PM2.5 was significantly associated with fuel type and location (p < 0.0001). Pairwise comparisons found significant differences between gas, indoor (geometric mean (GM): 79.32 μg/m3), and wood, indoor (GM: 541.14 μg/m3; p = 0.0002), and between wood, indoor, and outdoor (GM: 48.38 μg/m3; p = 0.0006) but not between gas, indoor, and outdoor (p = 0.56). For wood fuel homes, exposure peaks coincided with mealtimes. For LPG fuel homes, indoor levels may be explained by infiltration of ambient air pollution. In both wood and LPG fuel homes, PM2.5 levels exceeded the 24 h limit (25.0 µg/m3) proposed by the World Health Organization. Our findings suggest that increasing the adoption of LPG cookstoves and decreasing ambient air pollution in the Kathmandu valley will significantly lower daily PM2.5 exposures of brick workers and their families.

scholarly journals Long-term PM2.5 exposure and the clinical application of machine learning for predicting incident atrial fibrillation

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
In-Soo Kim ◽  
Pil-Sung Yang ◽  
Eunsun Jang ◽  
Hyunjean Jung ◽  
Seng Chan You ◽  
...  
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Air Pollution ◽  
General Population ◽  
Prediction Models ◽  
Fine Particulate Matter ◽  
Scoring Systems ◽  
Important Variable ◽  
Net Reclassification Improvement ◽  
Improvement Index

Abstract Clinical impact of fine particulate matter (PM2.5) air pollution on incident atrial fibrillation (AF) had not been well studied. We used integrated machine learning (ML) to build several incident AF prediction models that include average hourly measurements of PM2.5 for the 432,587 subjects of Korean general population. We compared these incident AF prediction models using c-index, net reclassification improvement index (NRI), and integrated discrimination improvement index (IDI). ML using the boosted ensemble method exhibited a higher c-index (0.845 [0.837–0.853]) than existing traditional regression models using CHA2DS2-VASc (0.654 [0.646–0.661]), CHADS2 (0.652 [0.646–0.657]), or HATCH (0.669 [0.661–0.676]) scores (each p < 0.001) for predicting incident AF. As feature selection algorithms identified PM2.5 as a highly important variable, we applied PM2.5 for predicting incident AF and constructed scoring systems. The prediction performances significantly increased compared with models without PM2.5 (c-indices: boosted ensemble ML, 0.954 [0.949–0.959]; PM-CHA2DS2-VASc, 0.859 [0.848–0.870]; PM-CHADS2, 0.823 [0.810–0.836]; or PM-HATCH score, 0.849 [0.837–0.860]; each interaction, p < 0.001; NRI and IDI were also positive). ML combining readily available clinical variables and PM2.5 data was found to predict incident AF better than models without PM2.5 or even established risk prediction approaches in the general population exposed to high air pollution levels.

scholarly journals A Survey on Recent Advances in Wearable Fall Detection Systems

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Anita Ramachandran ◽  
Anupama Karuppiah
Keyword(s):  
Machine Learning ◽  
World Health Organization ◽  
Fall Detection ◽  
Healthcare Systems ◽  
World Health ◽  
The European Union ◽  
Human Beings ◽  
Average Life Expectancy ◽  
Detection Systems ◽  
Health Organization

With advances in medicine and healthcare systems, the average life expectancy of human beings has increased to more than 80 yrs. As a result, the demographic old-age dependency ratio (people aged 65 or above relative to those aged 15–64) is expected to increase, by 2060, from ∼28% to ∼50% in the European Union and from ∼33% to ∼45% in Asia (Ageing Report European Economy, 2015). Therefore, the percentage of people who need additional care is also expected to increase. For instance, per studies conducted by the National Program for Health Care of the Elderly (NPHCE), elderly population in India will increase to 12% of the national population by 2025 with 8%–10% requiring utmost care. Geriatric healthcare has gained a lot of prominence in recent years, with specific focus on fall detection systems (FDSs) because of their impact on public lives. According to a World Health Organization report, the frequency of falls increases with increase in age and frailty. Older people living in nursing homes fall more often than those living in the community and 40% of them experience recurrent falls (World Health Organization, 2007). Machine learning (ML) has found its application in geriatric healthcare systems, especially in FDSs. In this paper, we examine the requirements of a typical FDS. Then we present a survey of the recent work in the area of fall detection systems, with focus on the application of machine learning. We also analyze the challenges in FDS systems based on the literature survey.

scholarly journals Model calculated global, regional and megacity premature mortality due to air pollution

2013 ◽  
Vol 13 (14) ◽  
pp. 7023-7037 ◽  
Author(s):  
J. Lelieveld ◽  
C. Barlas ◽  
D. Giannadaki ◽  
A. Pozzer
Keyword(s):  
Air Pollution ◽  
Human Life ◽  
Fine Particulate Matter ◽  
Premature Mortality ◽  
Mortality Rates ◽  
World Health ◽  
Model Calculations ◽  
Respiratory Mortality ◽  
Eu Directive ◽  
Global Rate

Abstract. Air pollution by fine particulate matter (PM2.5) and ozone (O3) has increased strongly with industrialization and urbanization. We estimate the premature mortality rates and the years of human life lost (YLL) caused by anthropogenic PM2.5 and O3 in 2005 for epidemiological regions defined by the World Health Organization (WHO). This is based upon high-resolution global model calculations that resolve urban and industrial regions in greater detail compared to previous work. Results indicate that 69% of the global population is exposed to an annual mean anthropogenic PM2.5 concentration of >10 μg m−3 (WHO guideline) and 33% to > 25 μg m−3 (EU directive). We applied an epidemiological health impact function and find that especially in large countries with extensive suburban and rural populations, air pollution-induced mortality rates have been underestimated given that previous studies largely focused on the urban environment. We calculate a global respiratory mortality of about 773 thousand/year (YLL ≈ 5.2 million/year), 186 thousand/year by lung cancer (YLL ≈ 1.7 million/year) and 2.0 million/year by cardiovascular disease (YLL ≈ 14.3 million/year). The global mean per capita mortality caused by air pollution is about 0.1% yr−1. The highest premature mortality rates are found in the Southeast Asia and Western Pacific regions (about 25% and 46% of the global rate, respectively) where more than a dozen of the most highly polluted megacities are located.

scholarly journals Association between airborne particles and meteorological parameters in Arak industrial city

2021 ◽  
Author(s):  
Maryam Salehi ◽  
Seyed Hamed Mirhoseini ◽  
Behroz Karimi ◽  
Amir Almasi Hashiani
Keyword(s):  
Air Pollution ◽  
Relative Humidity ◽  
Meteorological Parameters ◽  
Pollution Monitoring ◽  
World Health ◽  
Average Particle ◽  
Air Pollution Monitoring ◽  
The North ◽  
Pm2.5 And Pm10 ◽  
Arak City

Introduction: By crossing the borders of cities and countries, air pollution is now a global problem so that it can be claimed that there is no sound and clean air. This study aimed to investigate the effect of meteorological parameters on the concentration of particulate matter (PM2.5 and PM10) in the air of Arak city. Materials and methods: In this study, Arak city was divided into three regions using geographic information system (GIS). Based on air pollution monitoring stations in Arak city, it was tried to select one station from each region for analysis. Meteorological parameters including temperature (°C), relative humidity (٪), precipitation (mm), and wind speed (m/s), were obtained from Arak Meteorological Organization. Finally, the association between the concentration of PM (PM2.5 and PM10) and meteorological parameters were evaluated by SPSS. Results: Annual changes in PM (PM2.5 and PM10) showed that the average particle concentrations were 25.34 and 46.81 µg/m3 in the study periods, respectively. It was also found that the citizens of Arak were 2.5 times more exposed to PM (PM2.5 and PM10) pollutants than the standard recommended by the World Health Organization. Our findings also showed strong positive linear correlations of wind velocity and temperature with PM2.5 and PM10, as well  as relative humidity with PM10, and negative correlations of precipitation with PM2.5 and PM10, as well as relative humidity with PM2.5. Conclusion: The distribution map of Arak city indicated that the citizens of Shariati station and the governor's office were  facing high concentrations  of pollutants, posing them to a serious threat. Moreover, more pollution was recorded toward the north and northwest of the city. To protect the health of citizens in Arak, therefore, it is necessary to adopt appropriate policies and rules to reduce the concentrations of PM and other pollutants in the air of this city.

scholarly journals Machine learning approach for confirmation of COVID-19 cases: positive, negative, death and release

2020 ◽  
Vol 2 (3) ◽  
pp. 172-177
Author(s):  
Shawni Dutta ◽  
◽  
Samir Kumar Bandyopadhyay ◽  
Keyword(s):  
Infectious Disease ◽  
Machine Learning ◽  
Original Data ◽  
World Health ◽  
Learning Approach ◽  
Human Beings ◽  
Verification Method ◽  
Number Of Patients ◽  
Machine Learning Approach ◽  
The World

Introduction: Corona Virus Infectious Disease (COVID-19) is the infectious disease. The COVID-19 disease came to earth in early 2019. It is expanding exponentially throughout the world and affected an enormous number of human beings starting from the last month. The World Health Organization (WHO) on March 11, 2020 declared COVID-19 was characterized as “Pandemic”. This paper proposed approach for confirmation of COVID-19 cases after the diagnosis of doctors. The objective of this study uses machine learning method to evaluate how much predicted results are close to original data related to Confirmed-Negative-Released-Death cases of COVID-19. Materials and methods: For this purpose, a verification method is proposed in this paper that uses the concept of Deep-learning Neural Network. In this framework, Long shrt-term memory (LSTM) and Gated Recurrent Unit (GRU) are also assimilated finally for training the dataset. The prediction results are tally with the results predicted by clinical doctors. Results: The results are obtained from the proposed method with accuracy 87 % for the “confirmed Cases”, 67.8 % for “Negative Cases”, 62% for “Deceased Case” and 40.5 % for “Released Case”. Another important parameter i.e. RMSE shows 30.15% for Confirmed Case, 49.4 % for Negative Cases, 4.16 % for Deceased Case and 13.72 % for Released Case. Conclusions: The outbreak of Coronavirus has the nature of exponential growth and so it is difficult to control with limited clinical persons for handling a huge number of patients within a reasonable time. So it is necessary to build an automated model, based on machine learning approach, for corrective measure after the decision of clinical doctors.

scholarly journals Model calculated global, regional and megacity premature mortality due to air pollution

2013 ◽  
Vol 13 (3) ◽  
pp. 7737-7766
Author(s):  
J. Lelieveld ◽  
C. Barlas ◽  
D. Giannadaki ◽  
A. Pozzer
Keyword(s):  
Air Pollution ◽  
Human Life ◽  
Fine Particulate Matter ◽  
Premature Mortality ◽  
Health Impact ◽  
Mortality Rates ◽  
World Health ◽  
Model Calculations ◽  
Respiratory Mortality ◽  
Global Rate

Abstract. Air pollution by fine particulate matter (PM2.5) and ozone (O3) has increased strongly with industrialization and urbanization. We estimated the premature mortality rates and the years of human life lost (YLL) caused by anthropogenic PM2.5 and O3 in 2005 for epidemiological regions defined by the World Health Organization. We carried out high-resolution global model calculations to resolve urban and industrial regions in greater detail compared to previous work. We applied a health impact function to estimate premature mortality for people of 30 yr and older, using parameters derived from epidemiological cohort studies. Our results suggest that especially in large countries with extensive suburban and rural populations, air pollution-induced mortality rates have previously been underestimated. We calculate a global respiratory mortality of about 773 thousand yr−1 (YLL ≈ 5.2 million yr−1), 186 thousand yr−1 by lung cancer (YLL ≈ 1.7 million yr−1) and 2.0 million yr−1 by cardiovascular disease (YLL ≈ 14.3 million yr−1). The global mean per capita mortality caused by air pollution is about 0.1 % yr−1. The highest premature mortality rates are found in the Southeast Asia and Western Pacific regions (about 25% and 46% of the global rate, respectively) where more than a dozen of the most highly polluted megacities are located.

scholarly journals An Autonomous Air Pollution Monitoring System using IOT and Cloud

2019 ◽  
Vol 8 (4) ◽  
pp. 7220-7223
Keyword(s):  
Air Pollution ◽  
Internet Of Things ◽  
Air Pollutant ◽  
Cloud Services ◽  
Pollution Monitoring ◽  
World Health ◽  
Human Beings ◽  
Toxic Level ◽  
And Control ◽  
Internet Of Things Technology

Air pollution and its impact in the oceans and the terrain are in need of attention since it causes adverse effects in livelihood. Air pollutants identified so far produce destructive impacts to the human beings and the environment as well. The increase in toxic level reduces the capacity of the oceans to produce required oxygen which is a deteriorating factor. According to a recent report released by World Health Organization. 9 out of 10 people breathe the polluted air. Hence an efficient solution to monitor and control the air pollution is required. Recent trends in Internet of Things had helped in employing different gas sensors in order to identify the air pollutant levels. In this paper, it is proposed to develop a low cost system for efficient pollution monitoring and controlling. Integrated Internet of Things technology with Cloud services are employed to enable the effective services. Microsoft Azure’s cloud services are used to store the inferred data which is used for further communication. The pollutant’s toxicity level is identified and the system is alerted in order to control the air pollution. The system also uses the GSM / GPS module to track the location of high sensitivity within the selected zone. The toxic level of each type of pollutant is assessed. The main objective of the proposal is to observe, alert and control the air pollution.

scholarly journals Ambient PM2.5 Estimates and Variations during COVID-19 Pandemic in the Yangtze River Delta Using Machine Learning and Big Data

2021 ◽  
Vol 13 (8) ◽  
pp. 1423
Author(s):  
Debin Lu ◽  
Wanliu Mao ◽  
Lilin Zheng ◽  
Wu Xiao ◽  
Liang Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Big Data ◽  
Yangtze River ◽  
Regression Models ◽  
Yangtze River Delta ◽  
River Delta ◽  
The Yangtze River ◽  
The Yrd ◽  
The Yangtze River Delta

The lockdown of cities in the Yangtze River Delta (YRD) during COVID-19 has provided many natural and typical test sites for estimating the potential of air pollution control and reduction. To evaluate the reduction of PM2.5 concentration in the YRD region by the epidemic lockdown policy, this study employs big data, including PM2.5 observations and 29 independent variables regarding Aerosol Optical Depth (AOD), climate, terrain, population, road density, and Gaode map Point of interesting (POI) data, to build regression models and retrieve spatially continuous distributions of PM2.5 during COVID-19. Simulation accuracy of multiple machine learning regression models, i.e., random forest (RF), support vector regression (SVR), and artificial neural network (ANN) were compared. The results showed that the RF model outperformed the SVR and ANN models in the inversion of PM2.5 in the YRD region, with the model-fitting and cross-validation coefficients of determination R2 reached 0.917 and 0.691, mean absolute error (MAE) values were 1.026 μg m−3 and 2.353 μg m−3, and root mean square error (RMSE) values were 1.413 μg m−3, and 3.144 μg m−3, respectively. PM2.5 concentrations during COVID-19 in 2020 have decreased by 3.61 μg m−3 compared to that during the same period of 2019 in the YRD region. The results of this study provide a cost-effective method of air pollution exposure assessment and help provide insight into the atmospheric changes under strong government controlling strategies.

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