Prediction of PM2.5 concentration based on the similarity in air quality monitoring network

2018 ◽  
Vol 137 ◽  
pp. 11-17 ◽  
Author(s):  
Hong-di He ◽  
Min Li ◽  
Wei-li Wang ◽  
Zhan-yong Wang ◽  
Yu Xue
Keyword(s):  
Air Quality ◽  
Monitoring Network ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Pm2.5 Concentration

scholarly journals A homogenized daily in situ PM<sub>2.5</sub> concentration dataset from the national air quality monitoring network in China

2020 ◽  
Vol 12 (4) ◽  
pp. 3067-3080
Author(s):  
Kaixu Bai ◽  
Ke Li ◽  
Chengbo Wu ◽  
Ni-Bin Chang ◽  
Jianping Guo
Keyword(s):  
Air Quality ◽  
Monitoring Network ◽  
Change Point Detection ◽  
Quality Monitoring ◽  
Data Series ◽  
Air Quality Monitoring ◽  
Web Crawler ◽  
Haze Pollution ◽  
Pm2.5 Concentration

Abstract. In situ PM2.5 concentration observations have long been used as critical data sources in haze-related studies. Due to the frequently occurring haze pollution events, China started to regularly monitor PM2.5 concentration nationwide from the newly established air quality monitoring network in 2013. Nevertheless, the acquisition of these invaluable air quality samples is challenging given the absence of a publicly available data download interface. In this study, we provided a homogenized in situ PM2.5 concentration dataset that was created on the basis of hourly PM2.5 data retrieved from the China National Environmental Monitoring Center (CNEMC) via a web crawler between 2015 and 2019. Methods involving missing value imputation, change point detection, and bias adjustment were applied sequentially to deal with data gaps and inhomogeneities in raw PM2.5 observations. After excluding records with limited samples, a homogenized PM2.5 concentration dataset comprising of 1309 5-year long PM2.5 data series at a daily resolution was eventually compiled. This is the first attempt to homogenize in situ PM2.5 observations in China. The trend estimations derived from the homogenized dataset indicate a spatially homogeneous decreasing tendency of PM2.5 across China at a mean rate of about −7.6 % per year from 2015 to 2019. In contrast to raw PM2.5 observations, the homogenized data record not only has complete data integrity but is more consistent over space and time. This homogenized daily in situ PM2.5 concentration dataset is publicly accessible at https://doi.org/10.1594/PANGAEA.917557 (Bai et al., 2020a) and can be applied as a promising dataset for PM2.5-related studies such as satellite-based PM2.5 mapping, human exposure risk assessment, and air quality management.

scholarly journals A homogenized daily <i>in situ</i> PM<sub>2.5</sub> concentration dataset from national air quality monitoring network in China

2020 ◽  
Author(s):  
Kaixu Bai ◽  
Ke Li ◽  
Chengbo Wu ◽  
Ni-Bin Chang ◽  
Jianping Guo
Keyword(s):  
Air Quality ◽  
Monitoring Network ◽  
Ambient Air ◽  
Quality Monitoring ◽  
Homogeneity Test ◽  
Air Quality Monitoring ◽  
Haze Pollution ◽  
Temporal Coverage ◽  
Pm2.5 Concentration

Abstract. In situ PM2.5 concentration observations have long been used as critical data sources in haze related studies. Due to the frequently occurred haze pollution episodes, China started to establish the national ambient air quality monitoring network in 2012 but without providing a data download interface to the public. In this study, a five-year long homogenized daily in situ PM2.5 concentration dataset was generated on the basis of discrete data records retrieved from the China National Environmental Monitoring Center (CNEMC) via a web crawler. A set of methods for the purposes of gap filling, data merging, homogeneity test, and bias correction were geared up seamlessly to improve the data integrity and to make essential adjustments to the inherent discontinues detected in each PM2.5 concentration record. After excluding those records with limited temporal coverage, a dataset including 1,309 long-term coherent PM2.5 concentration time series at a daily resolution between 2015 and 2019 was carefully compiled. This is the first thrust to homogenize in situ PM2.5 observations in China. The trend estimations derived from the homogenized dataset indicate a spatially homogeneous decreasing tendency of PM2.5 across China at a mean rate of about −7.6 % per year from 2015 to 2019. Compared with the raw PM2.5 concentration dataset, the homogenized dataset not only has a complete temporal coverage but is more consistent over space and time. This homogenized daily in situ PM2.5 concentration dataset is publicly accessible at https://doi.org/10.1594/PANGAEA.917557, which can be used as a significant data source for satellite-based PM2.5 concentration mapping, population exposure risk assessment, and air quality monitoring and management.

scholarly journals A data-driven approach for optimal design of integrated air quality monitoring network in a chemical cluster

2018 ◽  
Vol 5 (9) ◽  
pp. 180889 ◽  
Author(s):  
Zhengqiu Zhu ◽  
Bin Chen ◽  
Sihang Qiu ◽  
Rongxiao Wang ◽  
Yiping Wang ◽  
...  
Keyword(s):  
Air Quality ◽  
Measurement Data ◽  
Monitoring Network ◽  
Quality Monitoring ◽  
Data Driven ◽  
Atmospheric Environment ◽  
Illustrative Case ◽  
Air Quality Monitoring ◽  
Detection Capability ◽  
Data Driven Approach

The chemical industry is of paramount importance to the world economy and this industrial sector represents a substantial income source for developing countries. However, the chemical plants producing inside an industrial district pose a great threat to the surrounding atmospheric environment and human health. Therefore, designing an appropriate and available air quality monitoring network (AQMN) is essential for assessing the effectiveness of deployed pollution-controlling strategies and facilities. As monitoring facilities located at inappropriate sites would affect data validity, a two-stage data-driven approach constituted of a spatio-temporal technique (i.e. Bayesian maximum entropy) and a multi-objective optimization model (i.e. maximum concentration detection capability and maximum dosage detection capability) is proposed in this paper. The approach aims at optimizing the design of an AQMN formed by gas sensor modules. Owing to the lack of long-term measurement data, our developed atmospheric dispersion simulation system was employed to generate simulated data for the above method. Finally, an illustrative case study was implemented to illustrate the feasibility of the proposed approach, and results imply that this work is able to design an appropriate AQMN with acceptable accuracy and efficiency.

scholarly journals Domain adaptation based deep calibration of low-cost PM2.5 sensors

2021 ◽  
Author(s):  
Sonu Kumar Jha ◽  
Mohit Kumar ◽  
Vipul Arora ◽  
Sachchida Nand Tripathi ◽  
Vidyanand Motiram Motghare ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Domain Adaptation ◽  
Low Cost ◽  
Monitoring Network ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Proposed Model ◽  
Temperature And Humidity ◽  
Calibration Approach

<div>Air pollution is a severe problem growing over time. A dense air-quality monitoring network is needed to update the people regarding the air pollution status in cities. A low-cost sensor device (LCSD) based dense air-quality monitoring network is more viable than continuous ambient air quality monitoring stations (CAAQMS). An in-field calibration approach is needed to improve agreements of the LCSDs to CAAQMS. The present work aims to propose a calibration method for PM2.5 using domain adaptation technique to reduce the collocation duration of LCSDs and CAAQMS. A novel calibration approach is proposed in this work for the measured PM2.5 levels of LCSDs. The dataset used for the experimentation consists of PM2.5 values and other parameters (PM10, temperature, and humidity) at hourly duration over a period of three months data. We propose new features, by combining PM2.5, PM10, temperature, and humidity, that significantly improved the performance of calibration. Further, the calibration model is adapted to the target location for a new LCSD with a collocation time of two days. The proposed model shows high correlation coefficient values (R2) and significantly low mean absolute percentage error (MAPE) than that of other baseline models. Thus, the proposed model helps in reducing the collocation time while maintaining high calibration performance.</div>

Identifying redundant monitoring stations in an air quality monitoring network

2018 ◽  
Vol 190 ◽  
pp. 256-268 ◽  
Author(s):  
Chenchen Wang ◽  
Laijun Zhao ◽  
Wenjun Sun ◽  
Jian Xue ◽  
Yujing Xie
Keyword(s):  
Air Quality ◽  
Monitoring Network ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Monitoring Stations
Sign in / Sign up

Export Citation Format

Share Document