# | Title | Journal | Year | Citations |
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1 | Application of k-means and hierarchical clustering techniques for analysis of air pollution: A review (1980–2019) | Atmospheric Pollution Research | 2020 | 307 |
2 | Urban air quality management-A review | Atmospheric Pollution Research | 2015 | 299 |
3 | Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions | Atmospheric Pollution Research | 2016 | 256 |
4 | Recursive neural network model for analysis and forecast of PM10 and PM2.5 | Atmospheric Pollution Research | 2017 | 235 |
5 | Assessment of air pollution around coal mining area: Emphasizing on spatial distributions, seasonal variations and heavy metals, using cluster and principal component analysis | Atmospheric Pollution Research | 2014 | 230 |
6 | Impact of technological innovation on CO2 emissions and emissions trend prediction on ‘New Normal’ economy in China | Atmospheric Pollution Research | 2019 | 215 |
7 | An LSTM-based aggregated model for air pollution forecasting | Atmospheric Pollution Research | 2020 | 199 |
8 | How do low wind speeds and high levels of air pollution support the spread of COVID-19? | Atmospheric Pollution Research | 2021 | 198 |
9 | Source apportionment and elemental composition of PM2.5 and PM10 in Jeddah City, Saudi Arabia | Atmospheric Pollution Research | 2012 | 179 |
10 | Air quality policy in the U.S. and the EU – a review | Atmospheric Pollution Research | 2015 | 174 |
11 | Chemical characterization of atmospheric PM in Delhi, India, during different periods of the year including Diwali festival | Atmospheric Pollution Research | 2011 | 171 |
12 | A review of standards and guidelines set by international bodies for the parameters of indoor air quality | Atmospheric Pollution Research | 2015 | 167 |
13 | Experimental investigation on the influence of titanium dioxide nanofluid on emission pattern of biodiesel in a diesel engine | Atmospheric Pollution Research | 2018 | 158 |
14 | Trace element composition of PM2.5 and PM10 from Kolkata – a heavily polluted Indian metropolis | Atmospheric Pollution Research | 2015 | 154 |
15 | The development and uses of EPA’s SPECIATE database | Atmospheric Pollution Research | 2010 | 142 |
16 | Industrial structure transformation and provincial heterogeneity characteristics evolution of air pollution: Evidence of a threshold effect from China | Atmospheric Pollution Research | 2020 | 139 |
17 | Air pollution by fine particulate matter in Bangladesh | Atmospheric Pollution Research | 2013 | 137 |
18 | Analysis and interpretation of particulate matter – PM10, PM2.5 and PM1 emissions from the heterogeneous traffic near an urban roadway | Atmospheric Pollution Research | 2010 | 129 |
19 | Ranking the suitability of common urban tree species for controlling PM2.5 pollution | Atmospheric Pollution Research | 2015 | 129 |
20 | Revising the use of potassium (K) in the source apportionment of PM2.5 | Atmospheric Pollution Research | 2013 | 125 |
21 | Impact of Middle Eastern Dust storms on human health | Atmospheric Pollution Research | 2017 | 125 |
22 | Spatio-temporal variation and influence factors of PM 2.5 concentrations in China from 1998 to 2014 | Atmospheric Pollution Research | 2017 | 122 |
23 | A novel hybrid-Garch model based on ARIMA and SVM for PM 2.5 concentrations forecasting | Atmospheric Pollution Research | 2017 | 121 |
24 | The impact of BTEX emissions from gas stations into the atmosphere | Atmospheric Pollution Research | 2012 | 120 |
25 | Characteristics of visibility and particulate matter (PM) in an urban area of Northeast China | Atmospheric Pollution Research | 2013 | 115 |
26 | Global warming projections to 2100 using simple CO2 greenhouse gas modeling and comments on CO2 climate sensitivity factor | Atmospheric Pollution Research | 2017 | 114 |
27 | Development of an ANN–based air pollution forecasting system with explicit knowledge through sensitivity analysis | Atmospheric Pollution Research | 2014 | 109 |
28 | Discovering relationships and forecasting PM10 and PM2.5 concentrations in Bogotá, Colombia, using Artificial Neural Networks, Principal Component Analysis, and k-means clustering | Atmospheric Pollution Research | 2018 | 109 |
29 | Impact of population aging and industrial structure on CO 2 emissions and emissions trend prediction in China | Atmospheric Pollution Research | 2018 | 108 |
30 | Airborne inhalable metals in residential areas of Delhi, India: distribution, source apportionment and health risks | Atmospheric Pollution Research | 2012 | 107 |
31 | Trace metal composition of airborne particulate matter in the coal mining and non–mining areas of Dhanbad Region, Jharkhand, India | Atmospheric Pollution Research | 2012 | 106 |
32 | An assessment of combustion, performance characteristics and emission control strategy by adding anti-oxidant additive in emulsified fuel | Atmospheric Pollution Research | 2018 | 102 |
33 | Dispersion model evaluation of PM2.5, NOx and SO2 from point and major line sources in Nova Scotia, Canada using AERMOD Gaussian plume air dispersion model | Atmospheric Pollution Research | 2013 | 101 |
34 | Characteristics and source apportionment of VOCs in the suburban area of Beijing, China | Atmospheric Pollution Research | 2016 | 101 |
35 | SPECIEUROPE: The European data base for PM source profiles | Atmospheric Pollution Research | 2016 | 100 |
36 | Phase distribution, sources and risk assessment of PAHs, NPAHs and OPAHs in a rural site of Pearl River Delta region, China | Atmospheric Pollution Research | 2014 | 98 |
37 | Forecasting of air quality in Delhi using principal component regression technique | Atmospheric Pollution Research | 2011 | 97 |
38 | Effects of land use and landscape pattern on PM2.5 in Yangtze River Delta, China | Atmospheric Pollution Research | 2018 | 95 |
39 | Applying machine learning methods in managing urban concentrations of traffic-related particulate matter (PM10 and PM2.5) | Atmospheric Pollution Research | 2019 | 95 |
40 | Chemical compositions and source identification of particulate matter (PM 2.5 and PM 2.5–10 ) from a scrap iron and steel smelting industry along the Ife–Ibadan highway, Nigeria | Atmospheric Pollution Research | 2015 | 94 |
41 | Indoor air quality investigation of the school environment and estimated health risks: Two-season measurements in primary schools in Kozani, Greece | Atmospheric Pollution Research | 2016 | 94 |
42 | Air quality predictions with a semi-supervised bidirectional LSTM neural network | Atmospheric Pollution Research | 2021 | 94 |
43 | Seasonal characteristics of ambient nitrogen oxides and ground–level ozone in metropolitan northeastern New Jersey | Atmospheric Pollution Research | 2012 | 91 |
44 | How many persistent organic pollutants should we expect? | Atmospheric Pollution Research | 2012 | 91 |
45 | MODIS aerosol optical depth observations over urban areas in Pakistan: quantity and quality of the data for air quality monitoring | Atmospheric Pollution Research | 2013 | 90 |
46 | The relationship between teleworking, traffic and air pollution | Atmospheric Pollution Research | 2018 | 90 |
47 | Impacts of urbanization-related factors on CO2 emissions: Evidence from China's three regions with varied urbanization levels | Atmospheric Pollution Research | 2018 | 89 |
48 | Survey of persistent organic pollutants (POPs) and polycyclic aromatic hydrocarbons (PAHs) in the atmosphere of rural, urban and industrial areas of Concepción, Chile, using passive air samplers | Atmospheric Pollution Research | 2012 | 87 |
49 | Receptor model based identification of PM2.5 sources in Canadian cities | Atmospheric Pollution Research | 2011 | 85 |
50 | Trends of BTEX in the central urban area of Iran: A preliminary study of photochemical ozone pollution and health risk assessment | Atmospheric Pollution Research | 2018 | 84 |