Abstract
In recent years, with rapid economic development, air pollution has become extremely serious, causing many negative effects on health, environment and medical costs. PM2.5 is one of the main components of air pollution. Therefore, it is necessary to know the PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 data and performs ensemble learning through three machine learning algorithms (Linear Regression, Random Forest, Gradient Boosting Decision Tree) to predict the PM2.5 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 PM2.5 forecasts and help the decision-maker to take proper action immediately.
Highlights
In recent years, with rapid economic development, air pollution has become increasingly serious, causing many negative effects on health, environment and medical costs
This study aims to use the PM2.5 sensor data provided by the open source community Location Aware Sensing System (LASS), and use the Spark big data computing framework and machine learning algorithms to build a real-time prediction model, perform real-time prediction of PM2.5 concentration value in order to achieve the purpose of PM2.5 early warning and air pollution monitoring
The effectiveness evaluation the four algorithms in whether of theensemble ensemble performance is higher than theresults single of regression, three algoPM2.5 concentration y(t + 1) value prediction from training data are shown in Table 5 berithms of linear regression, random forest regression, gradient boost, and the integration low
Summary
With rapid economic development, air pollution has become increasingly serious, causing many negative effects on health, environment and medical costs. The. World Health Organization’s (WHO) report mentions for about three-quarters of the world’s population, the air pollution concentration values of living environments exceeds those specified by the WHO, and indoor and outdoor air pollution causes about 7 million premature deaths every year [1]. Air pollution can cause many diseases and negatively affect human health. Martinelli, Olivieri and Girelli [2] have pointed out that exposure to fine suspended particulates (PM2.5 ) can lead to an increase in the incidence of cardiovascular diseases. IARC [3] mentioned that exposure to outdoor air pollution can cause lung cancer and increase the risk of bladder cancer and breast cancer.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International journal of environmental research and public health
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
