122 citations as recorded by crossref.

1. Urban Air Pollution Mapping Using Fleet Vehicles as Mobile Monitors and Machine Learning B. Zhao et al. https://doi.org/10.1021/acs.est.0c08034
2. High-resolution urban air pollution mapping J. Apte & C. Manchanda https://doi.org/10.1126/science.adq3678
3. Hotspots of black carbon and PM2.5 in an urban area and relationships to traffic characteristics A. Targino et al. https://doi.org/10.1016/j.envpol.2016.07.027
4. Characterization of Spatial Air Pollution Patterns Near a Large Railyard Area in Atlanta, Georgia H. Brantley et al. https://doi.org/10.3390/ijerph16040535
5. High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data J. Apte et al. https://doi.org/10.1021/acs.est.7b00891
6. A mobile car monitoring system as a supplementary tool for air quality monitoring in urban and rural environments: the case study from Poland M. Maslouski et al. https://doi.org/10.1038/s41598-023-43095-w
7. Advancements in Mobile-Based Air Pollution Detection: From Literature Review to Practical Implementation S. Sladojevic et al. https://doi.org/10.1155/2024/4895068
8. Continuous atmospheric in-situ measurements of the CH4/CO ratio at the Mt. Cimone station (Italy, 2165 m a.s.l.) and their possible use for estimating regional CH4 emissions C. Fratticioli et al. https://doi.org/10.1016/j.envres.2023.116343
9. Mobile and Fixed-Site Measurements To Identify Spatial Distributions of Traffic-Related Pollution Sources in Los Angeles M. Tessum et al. https://doi.org/10.1021/acs.est.7b04889
10. Spatial variation of fine particulate matter levels in Nairobi before and during the COVID-19 curfew: implications for environmental justice P. deSouza et al. https://doi.org/10.1088/2515-7620/ac1214
11. On-highway vehicle emission factors, and spatial patterns, based on mobile monitoring and absolute principal component score Y. Wen et al. https://doi.org/10.1016/j.scitotenv.2019.04.185
12. Urbanization and regional air pollution across South Asian developing countries – A nationwide land use regression for ambient PM2.5 assessment in Pakistan Y. Shi et al. https://doi.org/10.1016/j.envpol.2020.115145
13. High Resolution On-Road Air Pollution Using a Large Taxi-Based Mobile Sensor Network Y. Sun et al. https://doi.org/10.3390/s22166005
14. Short-term effectiveness of urban road-cleaning operations for fugitive road dust control: Evidence from TRAKER-based mobile monitoring S. Zhu et al. https://doi.org/10.1016/j.apr.2026.103190
15. Commuter exposure to black carbon particles on diesel buses, on bicycles and on foot: a case study in a Brazilian city A. Targino et al. https://doi.org/10.1007/s11356-017-0517-x
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18. Uncoupling Aviation and Roadway Contributions to Ultrafine Particle and Black Carbon Exposure in a Marginalized Neighborhood S. Sawan et al. https://doi.org/10.1021/acs.est.6c06666
19. SIBaR: a new method for background quantification and removal from mobile air pollution measurements B. Actkinson et al. https://doi.org/10.5194/amt-14-5809-2021
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21. Modelling nationwide spatial variation of ultrafine particles based on mobile monitoring J. Kerckhoffs et al. https://doi.org/10.1016/j.envint.2021.106569
22. Evaluating the Meteorological Effects on the Urban Form–Air Quality Relationship Using Mobile Monitoring Y. Tian et al. https://doi.org/10.1021/acs.est.1c04854
23. Evaluation of the Relationship between Meteorological Variables and NOx Emission Factors Based on Plume-Chasing Measurements S. Xiang et al. https://doi.org/10.1021/acsestengg.2c00317
24. Mobile measurement background quantification methods for determining local traffic emissions and background source contributions to ammonia W. Pu et al. https://doi.org/10.1016/j.atmosenv.2024.120523
25. Comparison of modeled traffic exposure zones using on–road air pollution measurements M. Shaibal et al. https://doi.org/10.5094/APR.2015.010
26. Predicting citywide distribution of air pollution using mobile monitoring and three-dimensional urban structure L. Cummings et al. https://doi.org/10.1016/j.scs.2021.103510
27. Baseline drift estimation for air quality data using quantile trend filtering H. Brantley et al. https://doi.org/10.1214/19-AOAS1318
28. Analysing and predicting the fine-scale distribution of traffic particulate matter in urban nonmotorized lanes by using wavelet transform and random forest methods B. Luo et al. https://doi.org/10.1007/s00477-023-02411-6
29. Reducing the Influence of Environmental Factors on Performance of a Diffusion-Based Personal Exposure Kit H. Zong et al. https://doi.org/10.3390/s21144637
30. Prediction of Short-Term Ultrafine Particle Exposures Using Real-Time Street-Level Images Paired with Air Quality Measurements J. Xu et al. https://doi.org/10.1021/acs.est.2c03193
31. Investigating the influence of urban land use and landscape pattern on PM2.5 spatial variation using mobile monitoring and WUDAPT Y. Shi et al. https://doi.org/10.1016/j.landurbplan.2019.04.004
32. Characterizing spatial variations of city-wide elevated PM10 and PM2.5 concentrations using taxi-based mobile monitoring Y. Yu et al. https://doi.org/10.1016/j.scitotenv.2022.154478
33. Spatially dense air pollutant sampling: Implications of spatial variability on the representativeness of stationary air pollutant monitors H. Li et al. https://doi.org/10.1016/j.aeaoa.2019.100012
34. Assessing the role of spatial aggregation schemes with varying campaign durations of mobile measurements on land use regression models for estimating nitrogen dioxide T. Tian et al. https://doi.org/10.1016/j.envpol.2025.125689
35. Socioeconomic Inequalities in PM2.5 Exposure and Local Source Contributions at Community Scales Using Hyper-Localized Taxi-Based Mobile Monitoring in Xi’an, China Y. Yu et al. https://doi.org/10.1021/acs.est.4c11385
36. Mobile monitoring of air pollution − a position paper on use cases, good practices, challenges, and opportunities J. Kerckhoffs et al. https://doi.org/10.1016/j.envint.2025.109582
37. Characterizing spatiotemporal distributions of black carbon and PM2.5 at a toll station: Observations on manual and electronic toll collection lanes Z. Luo et al. https://doi.org/10.1016/j.buildenv.2021.107933
38. High-Resolution Spatiotemporal Characterization of PM2.5 across On-Road Microenvironments Using Large-Scale Taxi-Based Mobile Monitoring in a Chinese City X. Ma et al. https://doi.org/10.1021/acs.est.5c16689
39. Overestimation of on-road air quality surveying data measured with a mobile laboratory caused by exhaust plumes of a vehicle ahead in dense traffic areas S. Woo et al. https://doi.org/10.1016/j.envpol.2016.08.065
40. Spatiotemporal correlation of urban pollutants by long-term measurements on a mobile observation platform S. Crocchianti et al. https://doi.org/10.1016/j.envpol.2020.115645
41. Automated identification of local contamination in remote atmospheric composition time series I. Beck et al. https://doi.org/10.5194/amt-15-4195-2022
42. Performance of Vehicle Add-on Mobile Monitoring System PM2.5 measurements during wildland fire episodes A. Bittner et al. https://doi.org/10.1039/D3EA00170A
43. Exploring the Holiday Effect on Elevated Traffic-Related Air Pollution with Hyperlocal Measurements in Chengdu, China S. Xiang et al. https://doi.org/10.3390/atmos16020171
44. Mobile air quality monitoring and comparison to fixed monitoring sites for instrument performance assessment A. Whitehill et al. https://doi.org/10.5194/amt-17-2991-2024
45. Detecting urban PM2.5 hotspots with mobile sensing and Gaussian process regression N. Perry et al. https://doi.org/10.1038/s44407-025-00038-1
46. Comparison of Ultrafine Particle and Black Carbon Concentration Predictions from a Mobile and Short-Term Stationary Land-Use Regression Model J. Kerckhoffs et al. https://doi.org/10.1021/acs.est.6b03476
47. A first screening of black carbon concentrations whilst commuting by diesel-fuelled buses in Montevideo, Uruguay A. Targino et al. https://doi.org/10.1016/j.heliyon.2025.e41697
48. On-Road Air Quality Associated with Traffic Composition and Street-Canyon Ventilation: Mobile Monitoring and CFD Modeling K. Kwak et al. https://doi.org/10.3390/atmos9030092
49. Field Assessment of the Village Green Project: An Autonomous Community Air Quality Monitoring System W. Jiao et al. https://doi.org/10.1021/acs.est.5b01245
50. Street View Imagery (SVI) in the Built Environment: A Theoretical and Systematic Review Y. Li et al. https://doi.org/10.3390/buildings12081167
51. Modelling pollutant and heat flow in a roadside column under ramped forcing: Reconciling seasonal PM2.5 patterns against survey data S. Das et al. https://doi.org/10.1016/j.icheatmasstransfer.2026.111620
52. Development of urban air monitoring with high spatial resolution using mobile vehicle sensors K. Yeom https://doi.org/10.1007/s10661-021-09139-2
53. Improving NO2 prediction by integrating tree diversity, urban form, and scale sensitivity through mobile monitoring Y. Tian et al. https://doi.org/10.1016/j.apgeog.2023.102943
54. Integration of air pollution data collected by mobile measurement to derive a preliminary spatiotemporal air pollution profile from two neighboring German-Czech border villages X. Liu et al. https://doi.org/10.1016/j.scitotenv.2020.137632
55. Ammonia and methane dairy emission plumes in the San Joaquin Valley of California from individual feedlot to regional scales D. Miller et al. https://doi.org/10.1002/2015JD023241
56. Real-time particle monitoring of pesticide drift from an axial fan airblast orchard sprayer M. Blanco et al. https://doi.org/10.1038/s41370-018-0090-5
57. Analysis of mobile monitoring data from the microAeth® MA200 for measuring changes in black carbon on the roadside in Augsburg X. Liu et al. https://doi.org/10.5194/amt-14-5139-2021
58. Mitigating air and noise pollution through highway capping: The Bundang-Suseo Highway Cap Project case study K. Park & J. Lee https://doi.org/10.1016/j.envpol.2024.123620
59. Mobile-platform measurement of air pollutant concentrations in California: performance assessment, statistical methods for evaluating spatial variations, and spatial representativeness P. Solomon et al. https://doi.org/10.5194/amt-13-3277-2020
60. Europe-Wide Spatial Models for Ultrafine Particles Based on Mobile Monitoring Y. Shen et al. https://doi.org/10.1021/acs.est.5c03380
61. New methods to derive street-scale spatial patterns of air pollution from mobile monitoring L. Padilla et al. https://doi.org/10.1016/j.atmosenv.2021.118851
62. Spatiotemporal Analysis of Complex Emission Dynamics in Port Areas Using High-Density Air Sensor Network J. Pan et al. https://doi.org/10.3390/toxics12100760
63. Characterizing on-road NO2 and particulate matter across urban road environments using city-scale mobile monitoring R. Cao et al. https://doi.org/10.1016/j.atmosenv.2026.122302
64. Spatiotemporal Characteristics and Driving Factors of Black Carbon in Augsburg, Germany: Combination of Mobile Monitoring and Street View Images X. Liu et al. https://doi.org/10.1021/acs.est.0c04776
65. The Impact of Traffic and Meteorology on Urban Particle Mass and Particle Number Concentrations: Student-Led Studies Using Mobile Measurements before, during, and after the COVID-19 Pandemic Lockdowns O. Klemm et al. https://doi.org/10.3390/atmos13010062
66. Methane mapping, emission quantification, and attribution in two European cities: Utrecht (NL) and Hamburg (DE) H. Maazallahi et al. https://doi.org/10.5194/acp-20-14717-2020
67. Estimating background PM2.5 concentrations using a wavelet-based convolution framework . Vikas et al. https://doi.org/10.1007/s10661-026-16082-7
68. Mobile monitoring for mapping spatial variation in urban air quality: Development and validation of a methodology based on an extensive dataset J. Van den Bossche et al. https://doi.org/10.1016/j.atmosenv.2015.01.017
69. Traffic-related air pollution near roadways: discerning local impacts from background N. Hilker et al. https://doi.org/10.5194/amt-12-5247-2019
70. Evaluating the Performance of Low-Cost PM2.5Sensors in Mobile Settings P. deSouza et al. https://doi.org/10.1021/acs.est.3c04843
71. City Scanner: Building and Scheduling a Mobile Sensing Platform for Smart City Services A. Anjomshoaa et al. https://doi.org/10.1109/JIOT.2018.2839058
72. Determination of local traffic emission and non-local background source contribution to on-road air pollution using fixed-route mobile air sensor network P. Wei et al. https://doi.org/10.1016/j.envpol.2021.118055
73. Adapting Public Annotated Data Sets and Low-Quality Dash Cameras for Spatiotemporal Estimation of Traffic-Related Air Pollution: A Transfer-Learning Approach Y. Fei et al. https://doi.org/10.1061/JCCEE5.CPENG-5667
74. Spatial-Temporal Assessment of Traffic-Related Pollutants Using Mobile and Stationary Monitoring in an Urban Environment M. Chavez et al. https://doi.org/10.3390/air3020018
75. Mobile Monitoring of Air Pollution Reveals Spatial and Temporal Variation in an Urban Landscape L. Cummings et al. https://doi.org/10.3389/fbuil.2021.648620
76. Comparison of Mobile and Fixed-Site Black Carbon Measurements for High-Resolution Urban Pollution Mapping S. Chambliss et al. https://doi.org/10.1021/acs.est.0c01409
77. Underestimated ammonia vehicular emissions in metropolitan city revealed by on-road mobile measurement C. Gu et al. https://doi.org/10.1088/1748-9326/acf94a
78. Smartphone Case-Based Gas Sensing Platform for On-site Acetone Tracking C. Xue et al. https://doi.org/10.1021/acssensors.2c00603
79. Low-power, open-path mobile sensing platform for high-resolution measurements of greenhouse gases and air pollutants L. Tao et al. https://doi.org/10.1007/s00340-015-6069-1
80. Use of mobile and passive badge air monitoring data for NOX and ozone air pollution spatial exposure prediction models W. Xu et al. https://doi.org/10.1038/jes.2016.9
81. The Green Heart Project: objectives, design, and methods A. Bhatnagar et al. https://doi.org/10.1093/aje/kwae458
82. Integrated Mobile Laboratory for Air Pollution Assessment: Literature Review and cc-TrAIRer Design C. Boanini et al. https://doi.org/10.3390/atmos12081004
83. Assessing air pollution and seasonal variations in Dong Nai Province, Vietnam: A geospatial analysis of key air quality parameters (2022−2023) X. Nguyen et al. https://doi.org/10.1016/j.uclim.2025.102475
84. Characterizing Global Methane Point-Source Emission Structures from Multi-Source Satellite Data and National Inventories: Implications for Differentiated Mitigation Pathways X. Su et al. https://doi.org/10.3390/rs18111765
85. Morphology–crowd coupling and mitigation of pedestrian-level PM2.5 exposure in summer night markets: interpretable machine learning and CFD across compact LCZs Y. Wang et al. https://doi.org/10.1016/j.buildenv.2026.115107
86. Mobile measurements of atmospheric pollutant concentrations in the pollutant plume of BER airport L. Gerling & S. Weber https://doi.org/10.1016/j.atmosenv.2023.119770
87. Characterizing the Spatial Variation of Air Pollutants and the Contributions of High Emitting Vehicles in Pittsburgh, PA Y. Tan et al. https://doi.org/10.1021/es5034074
88. Bus commuter exposure and the impact of switching from diesel to biodiesel for routes of complex urban geometry A. Targino et al. https://doi.org/10.1016/j.envpol.2020.114601
89. A spatial land use clustering framework for investigating the role of land use in mediating the effect of meteorology on urban air quality A. Montazeri et al. https://doi.org/10.1016/j.aeaoa.2021.100126
90. Urban land use regression models: can temporal deconvolution of traffic pollution measurements extend the urban LUR to suburban areas? K. Shairsingh et al. https://doi.org/10.1016/j.atmosenv.2018.10.013
91. Real-Time Monitoring of Spray Drift from Three Different Orchard Sprayers M. Blanco et al. https://doi.org/10.1016/j.chemosphere.2019.01.092
92. Inferred vehicular emissions at a near-road site: Impacts of COVID-19 restrictions, traffic patterns, and ambient air temperature D. Hall-Quinlan et al. https://doi.org/10.1016/j.atmosenv.2023.119649
93. Integrating Mobile and Fixed-Site Black Carbon Measurements to Bridge Spatiotemporal Gaps in Urban Air Quality C. Manchanda et al. https://doi.org/10.1021/acs.est.3c10829
94. Uncertainty in collocated mobile measurements of air quality A. Whitehill et al. https://doi.org/10.1016/j.aeaoa.2020.100080
95. Study of the Characteristics and Comprehensive Fuzzy Assessment of Indoor Air Chemical Contamination in Public Buildings X. Liang et al. https://doi.org/10.3389/fpubh.2021.579299
96. A new mobile monitoring approach to characterize community-scale air pollution patterns and identify local high pollution zones Y. Chen et al. https://doi.org/10.1016/j.atmosenv.2022.118936
97. Application of a Common Methodology to Select in Situ CO2 Observations Representative of the Atmospheric Background to an Italian Collaborative Network P. Trisolino et al. https://doi.org/10.3390/atmos12020246
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99. SENTINEL: A Shiny app for processing and analysis of fenceline sensor data M. MacDonald et al. https://doi.org/10.1016/j.envsoft.2025.106462
100. Identifying Patterns and Sources of Fine and Ultrafine Particulate Matter in London Using Mobile Measurements of Lung-Deposited Surface Area R. Shah et al. https://doi.org/10.1021/acs.est.2c08096
101. Characterizing Ultrafine Particle Mobile Monitoring Data for Epidemiology A. Doubleday et al. https://doi.org/10.1021/acs.est.3c00800
102. Identifying a regional aerosol baseline in the eastern North Atlantic using collocated measurements and a mathematical algorithm to mask high-submicron-number-concentration aerosol events F. Gallo et al. https://doi.org/10.5194/acp-20-7553-2020
103. Development of Air Quality Index Tool Based on Python Script and Geographic Information Systems H. Jumaah et al. https://doi.org/10.31127/tuje.1753853
104. Characterizing the spatial variability of local and background concentration signals for air pollution at the neighbourhood scale K. Shairsingh et al. https://doi.org/10.1016/j.atmosenv.2018.04.010
105. Variations in individuals’ exposure to black carbon particles during their daily activities: a screening study in Brazil A. Carvalho et al. https://doi.org/10.1007/s11356-018-2045-8
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107. Atmospheric CO2 and CH4 observations in Hangzhou before, during, and after the 2023 Asian Games: Insights from vehicle-carried and fixed stations T. Wang et al. https://doi.org/10.1016/j.apr.2025.102499
108. Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany J. Venkatraman Jagatha et al. https://doi.org/10.3390/s24134193
109. Design of an Air Pollution Monitoring Campaign in Beijing for Application to Cohort Health Studies S. Vedal et al. https://doi.org/10.3390/ijerph14121580
110. Identification of spikes associated with local sources in continuous time series of atmospheric CO, CO2 and CH4 A. El Yazidi et al. https://doi.org/10.5194/amt-11-1599-2018
111. Cyclists' exposure to air pollution under different traffic management strategies P. Krecl et al. https://doi.org/10.1016/j.scitotenv.2020.138043
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114. High-spatial-resolution mapping and source apportionment of aerosol composition in Oakland, California, using mobile aerosol mass spectrometry R. Shah et al. https://doi.org/10.5194/acp-18-16325-2018
115. Preface P. Solomon & S. Dhaniyala https://doi.org/10.1080/02786826.2020.1706973
116. Ambient air quality measurements from a continuously moving mobile platform: Estimation of area-wide, fuel-based, mobile source emission factors using absolute principal component scores T. Larson et al. https://doi.org/10.1016/j.atmosenv.2016.12.037
117. Unveiling the dynamic evolution of ambient formaldehyde along transport pathways in the Pearl River Delta Q. Chang et al. https://doi.org/10.1016/j.atmosenv.2026.122033
118. Characteristics of PM2.5 Chemical Species in 23 Chinese Cities Identified Using a Vehicular Platform H. Chen et al. https://doi.org/10.3390/su16062340
119. Investigation of vehicle cold start primary NO2 emissions inferred from ambient monitoring data in the UK and their implications for urban air quality V. Matthaios et al. https://doi.org/10.1016/j.atmosenv.2018.11.031
120. Effectiveness evaluation of water-sprinkling in controlling urban fugitive road dust based on TRAKER method: A case study in Baoding, China Y. Zhang et al. https://doi.org/10.1016/j.jes.2021.12.005
121. Towards hyperlocal source identification of pollutants in cities by combining mobile measurements with atmospheric modeling J. Lin et al. https://doi.org/10.1016/j.atmosenv.2023.119995
122. Exploratory study of atmospheric methane enhancements derived from natural gas use in the Houston urban area N. Sanchez et al. https://doi.org/10.1016/j.atmosenv.2018.01.001