Statistical modeling of O3, NOx, CO, PM2.5, VOCs and noise levels in commercial complex and associated health risk assessment in an academic institution

Singh, Deepak; Kumar, Amit; Kumar, Krishan; Singh, Bupender; Mina, Usha; Singh, Braj Bihari; Jain, V. K.

doi:10.1016/j.scitotenv.2016.08.086

Cited by 74 publications

(51 citation statements)

References 45 publications

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“…The instrument is sensitive to 200 VOC compounds and capable of estimating TVOC concentrations in the range of 1-10,000 ppb (resolution of 1 ppb), with a response time of 3 s and a sampling flow rate of 500 cc min -1 . The TVOC monitor was calibrated using 100 ppm isobutylene and zero air following the manufacturer's recommendations (Singh et al, 2016). NH 3 and H 2 S were measured using a MultiRAE Lite PGM-620X system (RAE Systems, San Jose, USA), within the concentration range of 0-100 ppm.…”

Section: Chemical Analysis and Data Treatmentmentioning

confidence: 99%

“…3 and 4 show that the TVOC concentrations in the wastewater treatment plant (indoor) are higher than those of the sports park (outdoor). This is because indoor levels are influenced by significant indoor sources or penetration from outdoor sources (Singh et al, 2016). In Table S1, the overall concentration of some parameters (PM 2.5 , H 2 S, NH 3 and TVOC) were higher in the primary clarifier.…”

Section: Level Of Tvocmentioning

confidence: 99%

See 1 more Smart Citation

Source Apportionment of Air Pollution and Characteristics of Volatile Organic Compounds in a Municipal Wastewater Treatment Plant, North Taiwan

Widiana

Tsai

Wang

et al. 2017

Aerosol Air Qual. Res.

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Monitoring air quality in the municipal wastewater treatment plants is an initial stage in preventing several problems related to air emissions. This study measured 103 volatile organic compounds (VOCs), total VOC (TVOC), and some prominent air pollutants (CO, CO 2 , NH 3 , H 2 S, PM 1 , PM 2.5 , PM 7 , PM 10 , TSP) in the municipal wastewater treatment plant X located in the north of Taiwan. Thirty-three VOCs were identified, which categorized as alkane, aromatic, alkene, ester, ether, haloalkane and ketone. Five dominant factors were determined from principal component analysis (PCA). The first factor involves indoor activities (such as particulate matter resuspension), and outdoor activities (such as vehicles exhausts), which explained 32.42% of total variance. Factor 2 was paint applications and domestic wastewater decomposition, with an explained variance of 17.23%. Factor 3 was solvent use, with an explained variance of 14.26%. Factor 4 was solvent use and road dust, with an explained variance of 8.54%. Factor 5 was a byproduct of the chlorination treatment process, with an explained variance of 6.92%. The five factors explained 79.37% of total variance. By applying absolute principal component scores (APCS), source apportionments were obtained having 35.21%, 26.04%, 16.13%, 7.03%, and 15.59% for factors 1, 2, 3, 4, and 5, respectively. Toluene had the highest ozone formation potential (OFP).

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Section: Chemical Analysis and Data Treatmentmentioning

confidence: 99%

Section: Level Of Tvocmentioning

confidence: 99%

Source Apportionment of Air Pollution and Characteristics of Volatile Organic Compounds in a Municipal Wastewater Treatment Plant, North Taiwan

Widiana

Tsai

Wang

et al. 2017

Aerosol Air Qual. Res.

View full text Add to dashboard Cite

show abstract

“…Moazami et al [23] developed a methodology based on uncertainty analysis by using a support vector machine for regression (SVR) model to predict the next day's concentration of CO. Different datasets were trained to find a suitable dataset to be used in the proposed methodology, which involved weather information and background pollutants such as CH4 and PM 10 . The authors compared their results with NN models.…”

Section: Previous Workmentioning

confidence: 99%

“…Many traffic emission assessment models have been reviewed by [10][11][12]. Traditional methods were based on experimental data using gas detectors and simulations.…”

Section: Introductionmentioning

confidence: 99%

Vehicular CO Emission Prediction Using Support Vector Regression Model and GIS

2018

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Transportation infrastructures play a significant role in the economy as they provide accessibility services to people. Infrastructures such as highways, road networks, and toll plazas are rapidly growing based on changes in transportation modes, which consequently create congestions near toll plaza areas and intersections. These congestions exert negative impacts on human health and the environment because vehicular emissions are considered as the main source of air pollution in urban areas and can cause respiratory and cardiovascular diseases and cancer. In this study, we developed a hybrid model based on the integration of three models, correlation-based feature selection (CFS), support vector regression (SVR), and GIS, to predict vehicular emissions at specific times and locations on roads at microscale levels in an urban areas of Kuala Lumpur, Malaysia. The proposed model comprises three simulation steps: first, the selection of the best predictors based on CFS; second, the prediction of vehicular carbon monoxide (CO) emissions using SVR; and third, the spatial simulation based on maps by using GIS. The proposed model was developed with seven road traffic CO predictors selected via CFS (sum of vehicles, sum of heavy vehicles, heavy vehicle ratio, sum of motorbikes, temperature, wind speed, and elevation). Spatial prediction was conducted based on GIS modelling. The vehicular CO emissions were measured continuously at 15 min intervals (recording 15 min averages) during weekends and weekdays twice per day (daytime, evening-time). The model's results achieved a validation accuracy of 80.6%, correlation coefficient of 0.9734, mean absolute error of 1.3172 ppm and root mean square error of 2.156 ppm. In addition, the most appropriate parameters of the prediction model were selected based on the CFS model. Overall, the proposed model is a promising tool for traffic CO assessment on roads.

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“…Different types of traffic CO prediction approaches are mentioned in the literature [10][11][12]. Early methods of traffic CO modelling were based on traditional techniques using data sampling and global position system (GPS) techniques.…”

Section: Introductionmentioning

confidence: 99%

Modeling of CO Emissions from Traffic Vehicles Using Artificial Neural Networks

et al. 2019

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Traffic emissions are considered one of the leading causes of environmental impact in megacities and their dangerous effects on human health. This paper presents a hybrid model based on data mining and GIS models designed to predict vehicular Carbon Monoxide (CO) emitted from traffic on the New Klang Valley Expressway, Malaysia. The hybrid model was developed based on the integration of GIS and the optimized Artificial Neural Network algorithm that combined with the Correlation based Feature Selection (CFS) algorithm to predict the daily vehicular CO emissions and generate prediction maps at a microscale level in a small urban area by using a field survey and open source data, which are the main contributions to this paper. The other contribution is related to the case study, which represents the spatial and quantitative variations in the vehicular CO emissions between toll plaza areas and road networks. The proposed hybrid model consists of three steps: the first step is the implementation of the correlation-based Feature Selection model to select the best model’s predictors; the second step is the prediction of vehicular CO by using a multilayer perceptron neural network model; and the third step is the creation of micro scale prediction maps. The model was developed using six traffic CO predictors: number of vehicles, number of heavy vehicles, number of motorbikes, temperature, wind speed and a digital surface model. The network architecture and its hyperparameters were optimized through a grid search approach. The traffic CO concentrations were observed at 15-min intervals on weekends and weekdays, four times per day. The results showed that the developed model had achieved validation accuracy of 80.6 %. Overall, the developed models are found to be promising tools for vehicular CO simulations in highly congested areas.

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Statistical modeling of O3, NOx, CO, PM2.5, VOCs and noise levels in commercial complex and associated health risk assessment in an academic institution

Cited by 74 publications

References 45 publications

Source Apportionment of Air Pollution and Characteristics of Volatile Organic Compounds in a Municipal Wastewater Treatment Plant, North Taiwan

Source Apportionment of Air Pollution and Characteristics of Volatile Organic Compounds in a Municipal Wastewater Treatment Plant, North Taiwan

Vehicular CO Emission Prediction Using Support Vector Regression Model and GIS

Modeling of CO Emissions from Traffic Vehicles Using Artificial Neural Networks

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