International Journal of Environmental Research and Public Health

Research

22 pages, 8505 KiB

Open AccessArticle

Design of a Spark Big Data Framework for PM_2.5 Air Pollution Forecasting

by Dong-Her Shih, Thi Hien To, Ly Sy Phu Nguyen, Ting-Wei Wu and Wen-Ting You

Int. J. Environ. Res. Public Health 2021, 18(13), 7087; https://doi.org/10.3390/ijerph18137087 - 2 Jul 2021

Cited by 9 | Viewed by 4502

In recent years, with rapid economic development, air pollution has become extremely serious, causing many negative effects on health, environment and medical costs. PM_2.5 is one of the main components of air pollution. Therefore, it is necessary to know the PM_2.5 [...] Read more.

In recent years, with rapid economic development, air pollution has become extremely serious, causing many negative effects on health, environment and medical costs. PM_2.5 is one of the main components of air pollution. Therefore, it is necessary to know the PM_2.5 air quality in advance for health. Many studies on air quality are based on the government’s official air quality monitoring stations, which cannot be widely deployed due to high cost constraints. Furthermore, the update frequency of government monitoring stations is once an hour, and it is hard to capture short-term PM_2.5 concentration peaks with little warning. Nevertheless, dealing with short-term data with many stations, the volume of data is huge and is calculated, analyzed and predicted in a complex way. This alleviates the high computational requirements of the original predictor, thus making Spark suitable for the considered problem. This study proposes a PM_2.5 instant prediction architecture based on the Spark big data framework to handle the huge data from the LASS community. The Spark big data framework proposed in this study is divided into three modules. It collects real time PM_2.5 data and performs ensemble learning through three machine learning algorithms (Linear Regression, Random Forest, Gradient Boosting Decision Tree) to predict the PM_2.5 concentration value in the next 30 to 180 min with accompanying visualization graph. The experimental results show that our proposed Spark big data ensemble prediction model in next 30-min prediction has the best performance (R² up to 0.96), and the ensemble model has better performance than any single machine learning model. Taiwan has been suffering from a situation of relatively poor air pollution quality for a long time. Air pollutant monitoring data from LASS community can provide a wide broader monitoring, however the data is large and difficult to integrate or analyze. The proposed Spark big data framework system can provide short-term PM_2.5 forecasts and help the decision-maker to take proper action immediately. Full article

(This article belongs to the Special Issue Climate Change and Health: Big Data Based Approach)

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16 pages, 2859 KiB

Open AccessArticle

A Comparative Assessment of Cooling Center Preparedness across Twenty-Five U.S. Cities

by Kyusik Kim, Jihoon Jung, Claire Schollaert and June T. Spector

Int. J. Environ. Res. Public Health 2021, 18(9), 4801; https://doi.org/10.3390/ijerph18094801 - 30 Apr 2021

Cited by 21 | Viewed by 4066

Abstract

Cooling centers have played a significant role in reducing the risks of adverse health impacts of extreme heat exposure. However, there have been no comparative studies investigating cooling center preparedness in terms of population coverage, location efficiency, and population coverage disparities among different [...] Read more.

Cooling centers have played a significant role in reducing the risks of adverse health impacts of extreme heat exposure. However, there have been no comparative studies investigating cooling center preparedness in terms of population coverage, location efficiency, and population coverage disparities among different subpopulation groups. Using a catchment area method with a 0.8 km walking distance, we compared three aspects of cooling center preparedness across twenty-five cities in the U.S. We first calculated the percentage of the population covered by a single cooling center for each city. Then, the extracted values were separately compared to the city’s heat indexes, latitudes, and spatial patterns of cooling centers. Finally, we investigated population coverage disparities among multiple demographics (age, race/ethnicity) and socioeconomic (insurance, poverty) subpopulation groups by comparing the percentage of population coverage between selected subpopulation groups and reference subpopulation groups. Our results showed that cooler cities, higher latitude cities, and cities with dispersed cooling centers tend to be more prepared than warmer cities, lower latitude cities, and cities with clustered cooling centers across the U.S. Moreover, older people (≥65) had 9% lower population coverage than younger people (≤64). Our results suggest that the placement of future cooling centers should consider both the location of other nearby cooling centers and the spatial distribution of subpopulations to maximize population coverage and reduce access disparities among several subpopulations. Full article

(This article belongs to the Special Issue Climate Change and Health: Big Data Based Approach)

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13 pages, 1926 KiB

Open AccessArticle

Analysis of Pneumonia Occurrence in Relation to Climate Change in Tanga, Tanzania

by Samweli Faraja Miyayo, Patrick Opiyo Owili, Miriam Adoyo Muga and Tang-Huang Lin

Int. J. Environ. Res. Public Health 2021, 18(9), 4731; https://doi.org/10.3390/ijerph18094731 - 29 Apr 2021

Cited by 8 | Viewed by 3675

Abstract

In 2018, 70% of global fatalities due to pneumonia occurred in about fifteen countries, with Tanzania being among the top eight countries contributing to these deaths. Environmental and individual factors contributing to these deaths may be multifaceted, but they have not yet been [...] Read more.

In 2018, 70% of global fatalities due to pneumonia occurred in about fifteen countries, with Tanzania being among the top eight countries contributing to these deaths. Environmental and individual factors contributing to these deaths may be multifaceted, but they have not yet been explored in Tanzania. Therefore, in this study, we explore the association between climate change and the occurrence of pneumonia in the Tanga Region, Tanzania. A time series study design was employed using meteorological and health data of the Tanga Region collected from January 2016 to December 2018 from the Tanzania Meteorological Authority and Health Management Information System, respectively. The generalized negative binomial regression technique was used to explore the associations between climate indicators (i.e., precipitation, humidity, and temperature) and the occurrence of pneumonia. There were trend differences in climate indicators and the occurrence of pneumonia between the Tanga and Handeni districts. We found a positive association between humidity and increased rates of non-severe pneumonia (incidence rate ratio (IRR) = 1.01; 95% CI: 1.01–1.02; p ≤ 0.05) and severe pneumonia (IRR = 1.02; 95% CI: 1.01–1.03; p ≤ 0.05). There was also a significant association between cold temperatures and the rate of severe pneumonia in Tanga (IRR = 1.21; 95% CI: 1.11–1.33; p ≤ 0.001). Other factors that were associated with pneumonia included age and district of residence. We found a positive relationship between humidity, temperature, and incidence of pneumonia in the Tanga Region. Policies focusing on prevention and control, as well as promotion strategies relating to climate change-related health effects should be developed and implemented. Full article

(This article belongs to the Special Issue Climate Change and Health: Big Data Based Approach)

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13 pages, 2647 KiB

Open AccessArticle

Analysis on Effectiveness of Impact Based Heatwave Warning Considering Severity and Likelihood of Health Impacts in Seoul, Korea

by Yeora Chae and Jongchul Park

Int. J. Environ. Res. Public Health 2021, 18(5), 2380; https://doi.org/10.3390/ijerph18052380 - 1 Mar 2021

Cited by 9 | Viewed by 3166

Abstract

Many countries are operating a heatwave warning system (HWWS) to mitigate the impact of heatwaves on human health. The level of heatwave warning is normally determined by using the threshold temperature of heat-related morbidity or mortality. However, morbidity and mortality threshold temperatures have [...] Read more.

Many countries are operating a heatwave warning system (HWWS) to mitigate the impact of heatwaves on human health. The level of heatwave warning is normally determined by using the threshold temperature of heat-related morbidity or mortality. However, morbidity and mortality threshold temperatures have not been used together to account for the severity of health impacts. In this study, we developed a heatwave warning system with two different warning levels: Level-1 and Level-2, by analyzing the severity and likelihood of heat-related morbidity and mortality using the generalized additive model. The study particularly focuses on the cases in Seoul, South Korea, between 2011 and 2018. The study found that the threshold temperature for heat-related morbidity and mortality are 30 °C and 33 °C, respectively. Approximately 73.1% of heat-related patients visited hospitals when temperature was between 30 °C and 33 °C. We validated the developed HWWS by using both the threshold temperatures of morbidity and mortality. The area under curves (AUCs) of the proposed model were 0.74 and 0.86 at Level-1 and Level-2, respectively. On the other hand, the AUCs of the model using only the mortality threshold were 0.60 and 0.86 at Level-1 and Level-2, respectively. The AUCs of the model using only the morbidity threshold were 0.73 and 0.78 at Level-1 and Level-2, respectively. The results suggest that the updated HWWS can help to reduce the impact of heatwaves, particularly on vulnerable groups, by providing the customized information. This also indicates that the HWWS could effectively mitigate the risk of morbidity and mortality. Full article

(This article belongs to the Special Issue Climate Change and Health: Big Data Based Approach)

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18 pages, 45689 KiB

Open AccessArticle

The Prediction of Hepatitis E through Ensemble Learning

by Tu Peng, Xiaoya Chen, Ming Wan, Lizhu Jin, Xiaofeng Wang, Xuejie Du, Hui Ge and Xu Yang

Int. J. Environ. Res. Public Health 2021, 18(1), 159; https://doi.org/10.3390/ijerph18010159 - 28 Dec 2020

Cited by 12 | Viewed by 2428

Abstract

According to the World Health Organization, about 20 million people are infected with Hepatitis E every year. In 2015, there were 44,000 deaths due to HEV infection worldwide. Food, water and climate are key factors that affect the outbreak of Hepatitis E. This [...] Read more.

According to the World Health Organization, about 20 million people are infected with Hepatitis E every year. In 2015, there were 44,000 deaths due to HEV infection worldwide. Food, water and climate are key factors that affect the outbreak of Hepatitis E. This paper presents an ensemble learning model for Hepatitis E prediction by studying the correlation between historical epidemic cases of hepatitis E and environmental factors (water quality and meteorological data). Environmental factors include many features, and ones that are most relevant to HEV are selected and input into the ensemble learning model composed by Gradient Boosting Decision Tree (GBDT) and Random Forest for training and prediction. Three indicators, root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE), are used to evaluate the effectiveness of the ensemble learning model against the classical time series prediction model. It is concluded that the ensemble learning model has a better prediction effect than the classical model, and the prediction effectiveness can be improved by exploiting water quality and meteorological factors (radiation, air pressure, precipitation). Full article

(This article belongs to the Special Issue Climate Change and Health: Big Data Based Approach)

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15 pages, 2509 KiB

Open AccessArticle

Indoor Thermal Environment Long-Term Data Analytics Using IoT Devices in Korean Apartments: A Case Study

by Hyunjun Yun, Jinho Yang, Byong Hyoek Lee, Jongcheol Kim and Jong-Ryeul Sohn

Int. J. Environ. Res. Public Health 2020, 17(19), 7334; https://doi.org/10.3390/ijerph17197334 - 8 Oct 2020

Cited by 5 | Viewed by 2943

Abstract

IoT-based monitoring devices can transmit real-time and long-term thermal environment data, enabling innovative conversion for the evaluation and management of the indoor thermal environment. However, long-term indoor thermal measurements using IoT-based devices to investigate health effects have rarely been conducted. Using apartments in [...] Read more.

IoT-based monitoring devices can transmit real-time and long-term thermal environment data, enabling innovative conversion for the evaluation and management of the indoor thermal environment. However, long-term indoor thermal measurements using IoT-based devices to investigate health effects have rarely been conducted. Using apartments in Seoul as a case study, we conducted long-term monitoring of thermal environmental using IoT-based real-time wireless sensors. We measured the temperature, relative humidity (RH), and CO₂ in the kitchen, living room, and bedrooms of each household over one year. In addition, in one of the houses, velocity and globe temperatures were measured for multiple summer and autumn seasons. Results of our present study indicated that outdoor temperature is an important influencing factor of indoor thermal environment and indoor RH is a good indicator of residents’ lifestyle. Our findings highlighted the need for temperature management in summer, RH management in winter, and kitchen thermal environment management during summer and tropical nights. This study suggested that IoT devices are a potential approach for evaluating personal exposure to indoor thermal environmental risks. In addition, long-term monitoring and analysis is an efficient approach for analyzing complex indoor thermal environments and is a viable method for application in healthcare. Full article

(This article belongs to the Special Issue Climate Change and Health: Big Data Based Approach)

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12 pages, 1224 KiB

Open AccessArticle

by Cinoo Kang, Chaerin Park, Whanhee Lee, Nazife Pehlivan, Munjeong Choi, Jeongju Jang and Ho Kim

Int. J. Environ. Res. Public Health 2020, 17(16), 5720; https://doi.org/10.3390/ijerph17165720 - 7 Aug 2020

Cited by 32 | Viewed by 4320

Abstract

Studies on the pattern of heatwave mortality using nationwide data that include rural areas are limited. This study aimed to assess the risk of heatwave-related mortality and evaluate the health risk-based definition of heatwave. We collected data on daily temperature and mortality from [...] Read more.

Studies on the pattern of heatwave mortality using nationwide data that include rural areas are limited. This study aimed to assess the risk of heatwave-related mortality and evaluate the health risk-based definition of heatwave. We collected data on daily temperature and mortality from 229 districts in South Korea in 2011–2017. District-specific heatwave-related mortality risks were calculated using a distributed lag model. The estimates were pooled in the total areas and for each urban and rural area using meta-regression. In the total areas, the threshold point of heatwave mortality risk was estimated at the 93rd percentile of temperature, and it was lower in urban areas than in rural areas (92nd percentile vs. 95th percentile). The maximum risk of heatwave-related mortality in the total area was 1.11 (95% CI: 1.01–1.22), and it was slightly greater in rural areas than in the urban areas (RR: 1.23, 95% CI: 0.99–1.53 vs. RR: 1.10, 95% CI: 1.01–1.20). The results differ by age- and cause-specific deaths. In conclusion, the patterns of heatwave-related mortality risk vary by area and sub-population in Korea. Thus, more target-specific heatwave definitions and action plans should be established according to different areas and populations. Full article

(This article belongs to the Special Issue Climate Change and Health: Big Data Based Approach)

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11 pages, 1320 KiB

Open AccessArticle

The Effects of Urban Containment Policies on Public Health

by Jeongbae Jeon, Solhee Kim and Sung Moon Kwon

Int. J. Environ. Res. Public Health 2020, 17(9), 3275; https://doi.org/10.3390/ijerph17093275 - 8 May 2020

Cited by 13 | Viewed by 3754

Abstract

Public health risks such as obesity are influenced by numerous personal characteristics, but the local spatial structure such as an area’s built environment can also affect the obesity rate. This study analyzes and discusses how a greenbelt plan as a tool of urban [...] Read more.

Public health risks such as obesity are influenced by numerous personal characteristics, but the local spatial structure such as an area’s built environment can also affect the obesity rate. This study analyzes and discusses how a greenbelt plan as a tool of urban containment policy has an effect on obesity. This study conducted spatial econometric regression models with five factors (13 variables) including transportation, socio-economic, public health, region, and policy factors. The relationship was analyzed between two policy effects of a greenbelt (i.e., a green buffer zone) and obesity. The variables for two policy effects of greenbelt zones are the size of the greenbelt and the inside and outside areas of the greenbelt. The results indicate that the two variables have negative effects on obesity. The results of the analyses in this study have several policy implications. Greenbelts play a role as an urban growth management policy, leading to a reduced obesity rate due to the influence of the transportation mode. In addition, greenbelts can also reduce the obesity rate because they provide recreation spaces for people. Full article

(This article belongs to the Special Issue Climate Change and Health: Big Data Based Approach)

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