Change Point Detection for Airborne Particulate Matter (PM2.5, PM10) by Using the Bayesian Approach

Abstract

Airborne particulate matter (PM) is a key air pollutant that affects human health adversely. Exposure to high concentrations of such particles may cause premature death, heart disease, respiratory problems, or reduced lung function. Previous work on particulate matter ( P M 2.5 and P M 10 ) was limited to specific areas. Therefore, more studies are required to investigate airborne particulate matter patterns due to their complex and varying properties, and their associated ( P M 10 and P M 2.5 ) concentrations and compositions to assess the numerical productivity of pollution control programs for air quality. Consequently, to control particulate matter pollution and to make effective plans for counter measurement, it is important to measure the efficiency and efficacy of policies applied by the Ministry of Environment. The primary purpose of this research is to construct a simulation model for the identification of a change point in particulate matter ( P M 2.5 and P M 10 ) concentration, and if it occurs in different areas of the world. The methodology is based on the Bayesian approach for the analysis of different data structures and a likelihood ratio test is used to a detect change point at unknown time (k). Real time data of particulate matter concentrations at different locations has been used for numerical verification. The model parameters before change point ( θ ) and parameters after change point ( λ ) have been critically analyzed so that the proficiency and success of environmental policies for particulate matter ( P M 2.5 and P M 10 ) concentrations can be evaluated. The main reason for using different areas is their considerably different features, i.e., environment, population densities, and transportation vehicle densities. Consequently, this study also provides insights about how well this suggested model could perform in different areas.

Analysis of the Environmental Effects of the Clean Heating Policy in Northern China

“…The policies they researched were early ones, such as in 2010, or in a specific city such as Beijing [14,15]. There are some methods for analyzing clean air policies, such as the Bayesian approach, life cycle assessment, the pollutant emission intensity generated by heating, the slack-based measure-directional distance function, the computable general equilibrium model, and the quasi-natural experiment method [14][15][16][17][18][19][20][21]. The above methods need to carry out a sophisticated social and economic system modeling for the research object.…”

Change Point Detection for Diversely Distributed Stochastic Processes Using a Probabilistic Method

“…A two stage change point detection technique in machine monitoring was suggested [8]. Bayesian Approach was used for change point detection of polluted days [9].…”