scholarly journals A Study on Indoor Particulate Matter Variation in Time Based on Count and Sizes and in Relation to Meteorological Conditions

2021 ◽  
Vol 13 (15) ◽  
pp. 8263
Author(s):  
Marius Bodor
Keyword(s):  
Particulate Matter ◽  
Meteorological Data ◽  
Monitoring Network ◽  
Pollution Monitoring ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Indoor Location ◽  
Particle Matter ◽  
Indoor Particulate Matter

An important aspect of air pollution analysis consists of the varied presence of particulate matter in analyzed air samples. In this respect, the present work aims to present a case study regarding the evolution in time of quantified particulate matter of different sizes. This study is based on data acquisitioned in an indoor location, already used in a former particulate matter-related article; thus, it can be considered as a continuation of that study, with the general aim to demonstrate the necessity to expand the existing network for pollution monitoring. Besides particle matter quantification, a correlation of the obtained results is also presented against meteorological data acquisitioned by the National Air Quality Monitoring Network. The transformation of quantified PM data in mass per volume and a comparison with other results are also addressed.

scholarly journals One-Year Simulation of Ozone and Particulate Matter in China Using WRF/CMAQ Modeling System

2016 ◽  
Author(s):  
Jianlin Hu ◽  
Jianjun Chen ◽  
Qi Ying ◽  
Hongliang Zhang
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Model Performance ◽  
Monitoring Network ◽  
Organic Aerosol ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Low Concentration ◽  
Temporal And Spatial

Abstract. China has been experiencing severe air pollution in recent decades. Although ambient air quality monitoring network for criteria pollutants has been constructed in over 100 cities since 2013 in China, the temporal and spatial characteristics of some important pollutants, such as particulate matter (PM) components, remain unknown, limiting further studies investigating potential air pollution control strategies to improve air quality and associating human health outcomes with air pollution exposure. In this study, a yearlong (2013) air quality simulation using the Weather Research & Forecasting model (WRF) and the Community Multi-scale Air Quality model (CMAQ) was conducted to provide detailed temporal and spatial information of ozone (O3), PM2.5 total and chemical components. Multi-resolution Emission Inventory for China (MEIC) was used for anthropogenic emissions and observation data obtained from the national air quality monitoring network were collected to validate model performance. The model successfully reproduces the O3 and PM2.5 concentrations at most cities for most months, with model performance statistics meeting the performance criteria. However, over-prediction of O3 generally occurs at low concentration range while under-prediction of PM2.5 happens at low concentration range in summer. Spatially, the model has better performance in Southern China than in Northern, Central and Sichuan basin. Strong seasonal variations of PM2.5 exist and wind speed and direction play important roles in high PM2.5 events. Secondary components have more boarder distribution than primary components. Sulfate (SO42−), nitrate (NO3−), ammonium (NH4+), and primary organic aerosol (POA) are the most important PM2.5 components. All components have the highest concentrations in winter except secondary organic aerosol (SOA). This study proves the ability of CMAQ model in reproducing severe air pollution in China, identifies the directions where improvements are needed, and provides information for human exposure to multiple pollutants for assessing health effects.

scholarly journals One-year simulation of ozone and particulate matter in China using WRF/CMAQ modeling system

2016 ◽  
Vol 16 (16) ◽  
pp. 10333-10350 ◽  
Author(s):  
Jianlin Hu ◽  
Jianjun Chen ◽  
Qi Ying ◽  
Hongliang Zhang
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Model Performance ◽  
Monitoring Network ◽  
Organic Aerosol ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Low Concentration ◽  
Temporal And Spatial

Abstract. China has been experiencing severe air pollution in recent decades. Although an ambient air quality monitoring network for criteria pollutants has been constructed in over 100 cities since 2013 in China, the temporal and spatial characteristics of some important pollutants, such as particulate matter (PM) components, remain unknown, limiting further studies investigating potential air pollution control strategies to improve air quality and associating human health outcomes with air pollution exposure. In this study, a yearlong (2013) air quality simulation using the Weather Research and Forecasting (WRF) model and the Community Multi-scale Air Quality (CMAQ) model was conducted to provide detailed temporal and spatial information of ozone (O3), total PM2.5, and chemical components. Multi-resolution Emission Inventory for China (MEIC) was used for anthropogenic emissions and observation data obtained from the national air quality monitoring network were collected to validate model performance. The model successfully reproduces the O3 and PM2.5 concentrations at most cities for most months, with model performance statistics meeting the performance criteria. However, overprediction of O3 generally occurs at low concentration range while underprediction of PM2.5 happens at low concentration range in summer. Spatially, the model has better performance in southern China than in northern China, central China, and Sichuan Basin. Strong seasonal variations of PM2.5 exist and wind speed and direction play important roles in high PM2.5 events. Secondary components have more boarder distribution than primary components. Sulfate (SO42−), nitrate (NO3−), ammonium (NH4+), and primary organic aerosol (POA) are the most important PM2.5 components. All components have the highest concentrations in winter except secondary organic aerosol (SOA). This study proves the ability of the CMAQ model to reproduce severe air pollution in China, identifies the directions where improvements are needed, and provides information for human exposure to multiple pollutants for assessing health effects.

Time Series Analysis for Prediction of PM2.5 Using Seasonal Autoregressive Integrated Moving Average (SARIMA) Model on Taiwan Air Quality Monitoring Network Data

2020 ◽  
Vol 17 (9) ◽  
pp. 3964-3969
Author(s):  
Doreswamy ◽  
K. S. Harish Kumar ◽  
Ibrahim Gad
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Moving Average ◽  
Monitoring Network ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Data Set ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average

Nowadays, in Taiwan, due to the increasing number of vehicles, industrialization of large energy consumption, uncontrolled constructions and urbanization, air pollution is becoming a major problem. Hence, it is necessary to control air pollution by applying air quality monitoring actions. The particulate matter (PM2.5) of the air pollution in TAQMN data is the main pollutant accountable for at least two-thirds of the severely polluted days in the major cities of Taiwan. In this work, machine learning (ML) techniques are widely used in developing models that can be used to control the air pollution. Seasonal Autoregressive Integrated Moving Average (SARIMA) model is used to predict the air pollution concentration, where the dataset chronologically from 2012 to 2016 are used to train the proposed method and testing data set from 2016 to 2017. The result of the SARIMA model shows high precision in forecasting the future values of particulate matter (P2.5) level with minimum error.

Modeling Dispersion of PM10 and PST in the Cesar Department Mining Region, Colombia by Using ISC and AERMOD

2010 ◽  
Author(s):  
Jose I. Huertas ◽  
Sebastia´n Izquierdo ◽  
Enrique D. Gonza´lez
Keyword(s):  
Air Quality ◽  
Emission Inventory ◽  
Meteorological Data ◽  
Correlation Coefficients ◽  
Monitoring Network ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Pollution Levels ◽  
Mining Region ◽  
High Degree

The mining region of the Cesar Department, Colombia, is made up of 6 mining companies with an approximate coal production of ∼3.5×107 tonnes/year through open cut exploitation. The region has an air quality monitoring network that reports readings exceeding the standard for the daily and annual concentration of PST and PM10. In order to orient the efforts of the decontamination program that has been implemented in the region, the environmental authority needs tools to model the PST and PM10 dispersion. Initially a unified PST and PM10 emission inventory methodology was developed and the topographic and meteorological information available for the region was collected. The dispersion of particled material was then modeled in ISC and AERMOD with meteorological data collected by 3 local stations during 2008. The results obtained were contrasted against the values measured by the air quality monitoring network that is operating in the region. Correlation coefficients were obtained exceeding 0.7, which is acceptable considering the high degree of uncertainty in emission inventory data. Based on the modeling results, the regions were delimited that, according to the local laws, correspond to areas with high, medium and moderate pollution levels. Finally, new actions were presented that make it possible to control PST and PM10 pollution in the mining region.

Indoor and outdoor air quality monitoring in hemp house — Case study

2011 ◽  
Vol 6 (3) ◽  
pp. 63-72 ◽  
Author(s):  
Jarmila Rimbalová ◽  
Silvia Vilčeková ◽  
Adriana Eštoková
Keyword(s):  
Air Quality ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Outdoor Air ◽  
Outdoor Air Quality ◽  
Indoor And Outdoor

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.

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