Trends and spatial patterns of fine-resolution aerosol optical depth–derived PM<sub>2.5</sub> emissions in the Northeast United States from 2002 to 2013

Tang, C.; Coull, Brent A.; Schwartz, Joel; Di, Qian; Koutrakis, Petros

doi:10.1080/10962247.2016.1218393

Cited by 10 publications

(6 citation statements)

References 17 publications

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“…The results obtained in the present study are comparable to some previous studies like Soni et al (2018), Sreekanth et al (2017, Chen et al (2013) and Shaw and Gorai (2018) etc. There are various other techniques like support vector regression (Kisi et al 2017;Liu et al 2017) and land use based regression techniques (Liu et al 2016;Tang et al 2017) used by many other studies and results obtained in those studies are comparable to the results obtained in the present study. Hence present methodology can be used for the estimation of PM 2.5 at ground level and other techniques can be used to compare and validate present results which can be considered as future scope.…”

Section: Discussionsupporting

confidence: 90%

“…The correlation was found to be significant, with a value of 0.65 during the study period. Tang et al (2017) observed trends in temporal and spatial patterns of PM 2.5 derived from satellite-derived AOD products over a study period of 2002-2013 in the north-eastern United States. AOD from MODIS, meteorological parameters like T, surface level BLH and RH are considered from North America Regional Reanalysis (NARR), and land use variables like vegetation and population are considered to develop a land use regression-based model.…”

Section: Introductionmentioning

confidence: 86%

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PM2.5 estimation using multiple linear regression approach over industrial and non-industrial stations of India

Gogikar

Tripathy

Maheswar

et al. 2020

J Ambient Intell Human Comput

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PM 2.5 (particulate matter size less than 2.5 µm, also called Respirable suspended particulate matter (RSPM)) is causing devastating effects on various living entities and is deleterious more than any other pollutants. As ambient air pollution is a scourge to India, in the present research work, PM 2.5 is considered and the current study aims to estimate surface level PM 2.5 concentrations using satellite-derived aerosol optical depth (AOD) along with meteorological data obtained from reanalysis and in-situ measurements over two different cities of India, namely: Agra, a non-industrial site for a study period of 2011-2015 and Rourkela, a highly industrialized location for 2009-2013, respectively. From the average daily variation of PM 2.5 , the pollution levels are critical and exceeding the threshold values defined by the pollution control board for most of the days at both the sites. Satellite-observed AOD values were also found to be very high over Agra (average AOD 0.76-0.8) and Rourkela (average AOD 0.4-0.46) during the study period. The annual exceedance factor (AEF) values over Agra and Rourkela were found to be always > 1.5 which indicates the above critical state of pollution. Traditional simple linear regression method (Model I), multiple linear regression (Model II (a-e)), log-linear regression (Model III) and conditional based MLR (Model IV and Model V) methods are applied to estimate the PM 2.5 concentrations over Taj for Agra region for a study period of 2011-2015 and Sonaparbat for Rourkela region for a study period of 2009-2013. The models obtained over Taj and Sonaparbat are applied to Rambagh (2011Rambagh ( -2015 and Rourkela (2009-2013) sites for validation. The coefficient of determination (R) between observed and estimated values are found to be statistically significant for model II (e) during training and validation at both the sites and model performance is adequate. The Model II (e) can thus be used as a unified explanatory model for the estimation of PM 2.5 over these two monitoring stations.

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Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 86%

PM2.5 estimation using multiple linear regression approach over industrial and non-industrial stations of India

Gogikar

Tripathy

Maheswar

et al. 2020

J Ambient Intell Human Comput

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“…Among these three factors, human activities, such as vehicle exhaust emission and industrial production 7 , 8 , are the dominant factors of PM 2.5 pollution 9 . Natural environment, such as meteorological conditions (e.g., precipitation and wind speed) 10 , facilitate the transportation and diffusion of PM 2.5 , while atmospheric chemical reactions stimulate the secondary formation of PM 2.5 11 . All of these factors interact with PM 2.5 at different spatial and temporal scales, and thus can have varying effects on PM 2.5 distribution.…”

Section: Introductionmentioning

confidence: 99%

Investigating PM2.5 responses to other air pollutants and meteorological factors across multiple temporal scales

Zhang

Liao

et al. 2020

Sci Rep

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It remains unclear on how PM2.5 interacts with other air pollutants and meteorological factors at different temporal scales, while such knowledge is crucial to address the air pollution issue more effectively. In this study, we explored such interaction at various temporal scales, taking the city of Nanjing, China as a case study. The ensemble empirical mode decomposition (EEMD) method was applied to decompose time series data of PM2.5, five other air pollutants, and six meteorological factors, as well as their correlations were examined at the daily and monthly scales. The study results show that the original PM2.5 concentration significantly exhibited non-linear downward trend, while the decomposed time series of PM2.5 concentration by EEMD followed daily and monthly cycles. The temporal pattern of PM10, SO2 and NO2 is synchronous with that of PM2.5. At both daily and monthly scales, PM2.5 was positively correlated with CO and negatively correlated with 24-h cumulative precipitation. At the daily scale, PM2.5 was positively correlated with O3, daily maximum and minimum temperature, and negatively correlated with atmospheric pressure, while the correlation pattern was opposite at the monthly scale.

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“…Regression models initially were used for predictive tasks. Tang et al [18] applied a linear regression method to predict PM2.5 emissions in the Northeast United States from 2002 to 2013 based on fine-resolution aerosol optical depth. Oteros et al [19] used different factors of pollen concentration and took into account the extreme weather events in the Mediterranean climate characteristics to establish a multivariate regression model.…”

Section: Related Workmentioning

confidence: 99%

Deep Hybrid Model Based on EMD with Classification by Frequency Characteristics for Long-Term Air Quality Prediction

et al. 2020

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Air pollution (mainly PM2.5) is one of the main environmental problems about air quality. Air pollution prediction and early warning is a prerequisite for air pollution prevention and control. However, it is not easy to accurately predict the long-term trend because the collected PM2.5 data have complex nonlinearity with multiple components of different frequency characteristics. This study proposes a hybrid deep learning predictor, in which the PM2.5 data are decomposed into components by empirical mode decomposition (EMD) firstly, and a convolutional neural network (CNN) is built to classify all the components into a fixed number of groups based on the frequency characteristics. Then, a gated-recurrent-unit (GRU) network is trained for each group as the sub-predictor, and the results from the three GRUs are fused to obtain the prediction result. Experiments based on the PM2.5 data from Beijing verify the proposed model, and the prediction results show that the decomposition and classification can develop the accuracy of the proposed predictor for air pollution prediction greatly.

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Trends and spatial patterns of fine-resolution aerosol optical depth–derived PM_2.5 emissions in the Northeast United States from 2002 to 2013

Cited by 10 publications

References 17 publications

PM2.5 estimation using multiple linear regression approach over industrial and non-industrial stations of India

PM2.5 estimation using multiple linear regression approach over industrial and non-industrial stations of India

Investigating PM2.5 responses to other air pollutants and meteorological factors across multiple temporal scales

Deep Hybrid Model Based on EMD with Classification by Frequency Characteristics for Long-Term Air Quality Prediction

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Trends and spatial patterns of fine-resolution aerosol optical depth–derived PM2.5 emissions in the Northeast United States from 2002 to 2013

Cited by 10 publications

References 17 publications

PM2.5 estimation using multiple linear regression approach over industrial and non-industrial stations of India

PM2.5 estimation using multiple linear regression approach over industrial and non-industrial stations of India

Investigating PM2.5 responses to other air pollutants and meteorological factors across multiple temporal scales

Deep Hybrid Model Based on EMD with Classification by Frequency Characteristics for Long-Term Air Quality Prediction

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Product

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Trends and spatial patterns of fine-resolution aerosol optical depth–derived PM_2.5 emissions in the Northeast United States from 2002 to 2013