Characterization of indoor settled dust and investigation of indoor air quality in different micro-environments

Sahu, Veerendra; Elumalai, Suresh Pandian; Gautam, Sneha; Singh, Nitin Kumar; Singh, P. K.

doi:10.1080/09603123.2018.1481498

Cited by 29 publications

(16 citation statements)

References 50 publications

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“…A significant correlation between and the meteorological variables was observed in the station HCH, which is the area with the highest concentration. Factors such as dust, population / area ratio and weather conditions have a predominant effect on concentration 56 . Figure 8 shows that there is a moderate positive correlation (0.39) between temperature and and a moderate negative correlation (-0.38) between relative humidity and .…”

Section: Resultsmentioning

confidence: 99%

Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru

Cordova

Portocarrero

Salas

et al. 2021

Sci Rep

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The prediction of air pollution is of great importance in highly populated areas because it directly impacts both the management of the city’s economic activity and the health of its inhabitants. This work evaluates and predicts the Spatio-temporal behavior of air quality in Metropolitan Lima, Peru, using artificial neural networks. The conventional feedforward backpropagation known as Multilayer Perceptron (MLP) and the Recurrent Artificial Neural network known as Long Short-Term Memory networks (LSTM) were implemented for the hourly prediction of $$\hbox {PM}_{10}$$ PM 10 based on the past values of this pollutant and three meteorological variables obtained from five monitoring stations. The models were validated using two schemes: The Hold-Out and the Blocked-Nested Cross-Validation (BNCV). The simulation results show that periods of moderate $$\hbox {PM}_{10}$$ PM 10 concentration are predicted with high precision. Whereas, for periods of high contamination, the performance of both models, the MLP and LSTM, were diminished. On the other hand, the prediction performance improved slightly when the models were trained and validated with the BNCV scheme. The simulation results showed that the models obtained a good performance for the CDM, CRB, and SMP monitoring stations, characterized by a moderate to low level of contamination. However, the results show the difficulty of predicting this contaminant in those stations that present critical contamination episodes, such as ATE and HCH. In conclusion, the LSTM recurrent artificial neural networks with BNCV adapt more precisely to critical pollution episodes and have better predictability performance for this type of environmental data.

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Section: Resultsmentioning

confidence: 99%

Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru

Cordova

Portocarrero

Salas

et al. 2021

Sci Rep

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“…Meanwhile, factors such as distance from dust source, frequency and duration of dust storms, vegetation cover, and soil type also have a greater influence on PM concentrations 7 . In areas of human activity, PM concentrations are accompanied by spatial shifts that are related to the frequency of human activity 11 . The increased concentration of PM to which individuals are exposed may alter metabolic activity and lead to serious diseases such as asthma, rhinitis, and tuberculosis 12 .…”

Section: Introductionmentioning

confidence: 99%

Health risk assessment of PM2.5 on walking trips

Zhu

Liu

et al. 2021

Sci Rep

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PM2.5 has an impact on residents' physical health during travelling, especially walking completely exposed to the environment. In order to obtain the specific impact of PM2.5 on walking, 368 healthy volunteers were selected and they were grouped according to gender and age. In the experiment, the heart rate change rate (HR%) is taken as test variable. According to receiver operating characteristic (ROC) curve, the travel is divided into two states: safety and risk. Based on this, a binary logit model considering Body Mass Index (BMI) is established to determine the contribution of PM2.5 concentration and body characteristics to travel risk. The experiment was conducted on Chang'an Middle Road in Xi'an City. The analysis results show that the threshold of HR% for safety and risk ranges from 31.1 to 40.1%, and that of PM2.5 concentration ranges from 81 to 168 μg/m3. The probability of risk rises 5.8% and 11.4%, respectively, for every unit increase in PM2.5 concentration and HR%. Under same conditions, the probability of risk for male is 76.8% of that for female. The probability of risk for youth is 67.5% of that for middle-aged people, and the probability of risk for people with BMI in healthy range is 72.1% of that for non-healthy range. The research evaluates risk characteristics of walking in particular polluted weather, which can improve residents’ health level and provide suggestions for travel decision while walking.

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“…Indoor PM concentrations can be higher than outside concentrations because of the combined effects of indoor and outdoor particles [6]. The components of indoor particles are determined by the particles generated in both indoor and outside environments [9]. Plants are a sustainable yet underexploited way to improve indoor air quality.…”

Section: Introductionmentioning

confidence: 99%

Particulate Matter (PM) Adsorption and Leaf Characteristics of Ornamental Sweet Potato (Ipomoea batatas L.) Cultivars and Two Common Indoor Plants (Hedera helix L. and Epipremnum aureum Lindl. & Andre)

et al. 2021

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Particulate matter (PM) is a serious threat to human health, climate, and ecosystems. Furthermore, owing to the combined influence of indoor and outdoor particles, indoor PM can pose a greater threat than urban PM. Plants can help to reduce PM pollution by acting as biofilters. Plants with different leaf characteristics have varying capacities to capture PM. However, the PM mitigation effects of plants and their primary factors are unclear. In this study, we investigated the PM adsorption and leaf characteristics of five ornamental sweet potato (Ipomea batatas L.) cultivars and two common indoor plants (Hedera helix L. and Epipremnum aureum Lindl. & Andre) exposed to approximately 300 μg m−3 of fly ash particles to assess the factors influencing PM adsorption on leaves and to understand the effects of PM pollution on the leaf characteristics of plants. We analyzed the correlation between PM adsorption and photosynthetic rate (Pn), stomatal conductance (gs), transpiration rate (Tr), leaf area (LA), leaf width/length ratio (W/L), stomatal density (SD), and stomatal pore size (SP). A Pearson’s correlation analysis and a principal component analysis (PCA) were used to evaluate the effects of different leaf characteristics on PM adsorption. The analysis indicated that leaf gas exchange factors, such as Pn and Tr, and morphological factors, such as W/L and LA, were the primary parameters influencing PM adsorption in all cultivars and species tested. Pn, Tr, and W/L showed a positive correlation with PM accumulation, whereas LA was negatively correlated.

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Characterization of indoor settled dust and investigation of indoor air quality in different micro-environments

Cited by 29 publications

References 50 publications

Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru

Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru

Health risk assessment of PM2.5 on walking trips

Particulate Matter (PM) Adsorption and Leaf Characteristics of Ornamental Sweet Potato (Ipomoea batatas L.) Cultivars and Two Common Indoor Plants (Hedera helix L. and Epipremnum aureum Lindl. & Andre)

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