scholarly journals The Development and Application of Machine Learning in Atmospheric Environment Studies

2021 ◽  
Vol 13 (23) ◽  
pp. 4839
Author(s):  
Lianming Zheng ◽  
Rui Lin ◽  
Xuemei Wang ◽  
Weihua Chen
Keyword(s):  
Machine Learning ◽  
Particulate Matter ◽  
Nitrogen Deposition ◽  
Air Pollutants ◽  
Model Performance ◽  
Atmospheric Science ◽  
Atmospheric Environment ◽  
Significant Progress ◽  
Quantitative Statistics

Machine learning (ML) plays an important role in atmospheric environment prediction, having been widely applied in atmospheric science with significant progress in algorithms and hardware. In this paper, we present a brief overview of the development of ML models as well as their application to atmospheric environment studies. ML model performance is then compared based on the main air pollutants (i.e., PM2.5, O3, and NO2) and model type. Moreover, we identify the key driving variables for ML models in predicting particulate matter (PM) pollutants by quantitative statistics. Additionally, a case study for wet nitrogen deposition estimation is carried out based on ML models. Finally, the prospects of ML for atmospheric prediction are discussed.

Vertical profiling of atmospheric air pollutants in rural India: A case study on particulate matter (PM10/PM2.5/PM1), carbon dioxide, and formaldehyde

Measurement ◽  
2021 ◽  
Vol 185 ◽  
pp. 110061
Author(s):  
Sneha Gautam ◽  
Cyril Sammuel ◽  
Aniket Bhardwaj ◽  
Zahra Shams Esfandabadi ◽  
M. Santosh ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Particulate Matter ◽  
Air Pollutants ◽  
Rural India ◽  
Atmospheric Air ◽  
Vertical Profiling

scholarly journals A Time-Based Assessment of Particulate Matter (PM2.5) Levels at a Highly Trafficked Intersection: Case Study of Sango-Ota, Nigeria

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 532
Author(s):  
David Olukanni ◽  
David Enetomhe ◽  
Gideon Bamigboye ◽  
Daniel Bassey
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Health Risks ◽  
Air Pollutants ◽  
Air Contamination ◽  
Ogun State ◽  
Sight Distance ◽  
Volatile Organic ◽  
Road Corridors

Vehicle emissions have become one of the most prevailing air contamination sources, including nitrogen oxides, volatile organic compounds, carbon monoxide and particulate matter (PM). Among other air pollutants, PM limits visible sight distance and poses health risks upon inhalation into the human body. This study focused on assessing PM2.5 concentrations in air at different periods of the day at the highly trafficked grade-separated intersection of Sango-Ota, Ogun State, Nigeria. PM2.5 readings were taken at three at-grade points around the intersection’s roundabout between 10:00 a.m. and 5:00 p.m. for four (4) days using the BR-SMART-126 Portable 4-in-1 air quality monitor. The highest level of PM2.5 obtained on Day 1 (Monday) and Day 4 (Thursday) was about 45.1% and 38.6%, respectively, lower than that of Day 3 (Wednesday). The highest concentrations of PM2.5 were recorded between 11:00 and 13:00 and between 16:00 and 18:00 (up to 217 µg/m3) whereas the lowest levels were recorded between 14:00 and 15:00 (as low as 86 µg/m3). The concentration of PM2.5 at the Sango-Ota intersection is adjudged “very poor” with average hourly concentrations between 97 and 370 µg/m3. Outcomes obtained indicate the need for improved measures to control air quality along major road corridors and at intersections in Ogun State and Nigeria at large.

scholarly journals Caracterização e Avaliação da Toxicidade de MP10 Presentes na Área Urbana de Catalão – GO Associados a Parâmetros Climatológicos

2020 ◽  
Vol 14 (27) ◽  
pp. 37-48
Author(s):  
Marcos E. G. do Carmo ◽  
Fernanda C. da C. Kunizaki ◽  
Nara L. da S. Sousa ◽  
Lincoln L. Romualdo
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Urban Area ◽  
Air Pollutants ◽  
Hospital Admissions ◽  
Urban Traffic ◽  
Atmospheric Environment ◽  
Environmental Research ◽  
Airborne Bacteria ◽  
Mato Grosso

Este trabalho apresenta valores da variação de concentração, caracterização elementar e avaliação da toxidade in vitro no material particulado 10 μm (MP10) coletado em dois sítios na área urbana de Catalão-GO. O período amostrado foi entre 05/08/17 a 28/03/18. Os valores de concentração de MP10 estavam em conformidade com a legislação do CONAMA. A espectrometria de fluorescência de raios X identificou a presença majoritária de ferro e menores quantidades de fósforo e enxofre, caracterizando o MP10como resultado da ressuspenção de solo, emissão veicular e emissões do setor industrial. No estudo de toxidade verificou-se que o MP10 não inibiu o desenvolvimento de culturas bacterianas. Referências 1. Queiroz, P. G. M.; Jacomino, V. M. F.; Menezes, M. A. B.; Composição elementar do material particulado presente no aerossol atmosférico do município de Sete Lagoas, Minas Gerais. Química Nova, 2007, 30, 1233.2. Kim, K. H; Jahan, S. A.; Kabir, E. A review on human health perspective of air pollution with respect to allergies and asthma. Environment International, 2013, 59, 41. 3. Padula, A.; Yang, W.; Lurmann, F.; Balmes, J.; Hammond, S.; Shaw, G.; Prenatal exposure to air pollution, maternal diabetes and preterm birth, Environmental Research, 2019, 170, 160. 4. Binaku, K.; O’Brien, T.; Schmeling, M.; Fosco, T.; Statistical analysis of aerosol species, trace gasses, and meteorology in Chicago, Environmental Monitoring and Assessment, 2013, 185, 7295. 5. Almeida-Silva, M.; Canha, N.; Freitas, M. C.; Dung, H. M.; Dionísio, I.; Air pollution at an urban traffic tunnel in Lisbon, Portugal-an INNA study. Applied Radiation and Isotopes, 2011, 69, 1586.6. Marloes, E.; Gerard, H.; Olena, G. Molter, A.; Agius, Raymond.; Beelen, R.; Brunekreef, B.; Custovic, A.; Cyrys, J.; Fuertes, E.; Heinrich, J. Hoffmann, B.; Hoogh, K.; Jedynska, A.; Keuken, M.; Klumper, C.; Kooter, I.; Kramer, U.; Korek, M.; Koppelman, G. H.; Kuhlbusch, T. A. J.; Simpson, A.; Smit, H.A.; Tsai, M.; Wang, M.; Wolf, K.; Pershagen, G.; Gehring, U.; Elemental Composition of Particulate Matter and the Association with Lung Function. Empidemiology, 2014, 25, 648. 7. Baird, C.; Química Ambiental, Bookman: Porto Alegre, 2002.8. Ruckerl, R.; Schneider, A.; Breitner, S,; et. al. Health effects of particulate air pollution: A review of epidemiological evidence. Inhal Toxicol, 2011, 23, 555.9. Gavinier S, Nascimento L. Particulate matter and hospital admissions due to ischemic heart disease in Sorocaba, SP. Rev. Ambient. Água. 2014, 8, 228. 10. Nascimento, L. Air pollution and cardiovascular hospital admissions in a medium-sized city in São Paulo State, Brazil. Braz J Med Biol Res. 2011, 44, 720.11. Machin, A.; Nascimento L. Effects of exposure to air pollutants on children’s health in Cuiabá, Mato Grosso State, Brazil. Cad Saúde Pública [online], 2018, 34. 12. Liu, H.; Dunea, D.; Iordache, S.; Pohoata, A. A Review of Airborne Particulate Matter Effects on Young Children’s Respiratory Symptoms and Diseases. Atmosphere, 2018, 9, 150. 13. Grineski, S.; Collins, T.; Morales, D.; Asian Americans and disproportionate exposure to carcinogenic hazardous air pollutants: A national study, Social Science e Medicine, 2017, 185, 71. 14. Mutlu, E.; Comba, I.; Cho, T.; Engen, P.; Yazici, C.; Soberanes, S.; Hamanaka, R.; Nigdelioglu, R.; Meliton, A.; Ghio, A.; Budinger, S.; Mutlu, G.; Inhalational exposure to particulate matter air pollution alters the composition of the gut microbiome, Environmental Pollution, 2018, 240, 817. 15. Shah, M.; Shaheen-Nazir, R. Assessment of the trace elements level in urban atmospheric particulate matter and source apportionment Islamabad, Pakistan. Atmospheric Pollution Research, 2012, 3, 39.16. Vellingiri, K.; Kim, K.; Ma, C.; Kang, C.; Lee, J.; Kim, I.; Brown, R.; Ambient particulate matter in a central urban area of Seoul, Korea. Chemosphere, 2015, 119, 812.17. Hassan, H.; Kumar, P.; Kakosimos, K.; Flux estimation of fugitive particulate matter emissions from loose Calcisols at construction sites, Atmospheric Environment, 2016, 141, 96. 18. Caixeta, D.; Silva T.; Santana, F.; Almeida, W.; Quality monitoring indoor air of a school of public network located in the city of Cuiaba-MT. Engineering and Science, 2016, 1, 20.19. Smets, W.; Moretti, S.; Denys, S. Airborne bacteria in the atmosphere: Presence, purpose, and potential. Atmospheric Environment, 2016, 139, 214. 20. Maki, T.; Hara, K.; Kobayashi, F. et al. Vertical distribution of airborne bacterial communities in an Asian-dust downwind area, Noto Peninsula. Atmospheric Environment, 2015, 119, 282. 21. Maki, T.; Kakikawa, M.; Kobayashi, F; Yamada, M.; Atsushi, M.; Hasegawa, H.; Iwasaka, Y.; Assessment of composition and origin of airborne bacteria in the free troposphere over Japan. Atmospheric Environment, 2013, 74, 73. 22. Pereira, P.; Lopes, W.; Carvalho, L.; Rocha, G.; Bahia, N.; Loyola, J.; Quiterio, S.; Escaleira, V.; Arbilla, G.; Andrade, J.; Atmospheric concentrations and dry deposition fluxes of particulate trace metals in Salvador, Bahia, Brazil, Atmospheric Environment, 2007, 41, 7837. 23. Romualdo, L.; Santos, R.; Lima, F.; Andrade, L.; Ferreira, I.; Pozza, S.; Environmental Impact Monitoring of a Minero-Chemical Complex in Catalão Urban Area of PTS, PM10 and PM2.5 by EDX Characterization, Chemical Engineering transactions, 2015, 43, 1909.24. Sousa, N.; Análise físico-química e toxicidade do material particulado (MP10) no ar atmosférico em Catalão – GO, Dissertação (Mestrado) - Curso de Química, Universidade Federal de Goiás, Catalão, 2018, 87.25. SILVA, A. C. N.; BERNARDES, R. S.; MORAES, L. R. S.; DOS REIS, J. D. P. “Critérios adotados para seleção de indicadores de contaminação ambiental relacionados aos resíduos sólidos de serviços de saúde: uma proposta de avaliação”. Cad. Saúde Pública, 18:1401-1409, 2002.26. Morris, A.; Beck, J.; Schloss, P.; Campbell, T.; Crothers, K.; Curtis, J.; Flores, S.; Fontenot, A.; Ghedin, E.; Huang, L.; Jabloski, K.; Kleerup, E.; Lynch, S.; Sodergreen, E.; Twigg, H.; Young, V.; Bassis, C.; Venkataraman, A.; Schmidt, T.; Weinstock, G.;. Comparison of the respiratory microbiome in healthy nonsmokers and smokers, American Jounal Respiratory and Critical Care Medicine, 2013, 15, 1067.

scholarly journals Transferability of a Machine Learning-Based Model of Hourly Traffic Volume Estimation—Florida and New Hampshire Case Study

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Przemysław Sekuła ◽  
Zachary Vander Laan ◽  
Kaveh Farokhi Sadabadi ◽  
Krzysztof Kania ◽  
Sara Zahedian
Keyword(s):  
Machine Learning ◽  
Model Performance ◽  
Traffic Volume ◽  
Volume Estimation ◽  
Time Period ◽  
Traffic Volumes ◽  
Processing Effort ◽  
The Cost ◽  
Traffic Volume Estimation

This paper focuses on the problem of model transferability for machine learning models used to estimate hourly traffic volumes. The presented findings enable not only an increase in the accuracy of existing models but also, simultaneously, reduce the cost of data needed for training the models—making statewide traffic volume estimation more economically feasible. Previous research indicates that machine learning volume estimation models that leverage GPS probe data can provide transportation agencies with accurate estimates of hourly traffic volumes—which are fundamental for both operational and planning purposes—and do so with a higher level of accuracy than the prevailing profiling method. However, this approach requires a large dataset for model calibration (i.e., input and continuous count station data), which involves significant monetary investment and data-processing effort. This paper proposes solutions, which allow the model to be prepared using a much smaller dataset, given that a previously collected dataset, which may be gathered in a different place and time period, exists. Based on a broad selection of experiments, the results indicate that the proposed approach is capable of achieving similar model performance while collecting data for a 5 times shorter time period and utilizing 1/4 of the number of continuous count stations. These findings will help reduce the cost of preparing and maintaining the traffic volume models and render the traffic volume estimations more financially appealing.

scholarly journals Optimizing the Environmental and Economic Sustainability of Remote Community Infrastructure

2020 ◽  
Vol 12 (6) ◽  
pp. 2208 ◽  
Author(s):  
Jamie E. Filer ◽  
Justin D. Delorit ◽  
Andrew J. Hoisington ◽  
Steven J. Schuldt
Keyword(s):  
Environmental Impacts ◽  
Sustainability Assessment ◽  
Model Performance ◽  
Assessment Model ◽  
Remote Communities ◽  
Remote Community ◽  
Rural Villages ◽  
Sustainable Technologies ◽  
Study Results

Remote communities such as rural villages, post-disaster housing camps, and military forward operating bases are often located in remote and hostile areas with limited or no access to established infrastructure grids. Operating these communities with conventional assets requires constant resupply, which yields a significant logistical burden, creates negative environmental impacts, and increases costs. For example, a 2000-member isolated village in northern Canada relying on diesel generators required 8.6 million USD of fuel per year and emitted 8500 tons of carbon dioxide. Remote community planners can mitigate these negative impacts by selecting sustainable technologies that minimize resource consumption and emissions. However, the alternatives often come at a higher procurement cost and mobilization requirement. To assist planners with this challenging task, this paper presents the development of a novel infrastructure sustainability assessment model capable of generating optimal tradeoffs between minimizing environmental impacts and minimizing life-cycle costs over the community’s anticipated lifespan. Model performance was evaluated using a case study of a hypothetical 500-person remote military base with 864 feasible infrastructure portfolios and 48 procedural portfolios. The case study results demonstrated the model’s novel capability to assist planners in identifying optimal combinations of infrastructure alternatives that minimize negative sustainability impacts, leading to remote communities that are more self-sufficient with reduced emissions and costs.

Exercise Performed Concomitantly with Particulate Matter Exposure Inhibits Lung Injury

2017 ◽  
Vol 39 (02) ◽  
pp. 133-140 ◽  
Author(s):  
Adriano Silva-Renno ◽  
Guilherme Baldivia ◽  
Manoel Oliveira-Junior ◽  
Maysa Brandao-Rangel ◽  
Elias El-Mafarjeh ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Systemic Inflammation ◽  
Air Pollutants ◽  
Lung Parenchyma ◽  
Treadmill Training ◽  
Tnf Alpha ◽  
Systemic Response ◽  
Pm 2.5 ◽  
Pm 10 ◽  
Particulate Matter Exposure

AbstractAir pollution is a growing problem worldwide, inducing and exacerbating several diseases. Among the several components of air pollutants, particulate matter (PM), especially thick (10–2.5 µm; PM 10) and thin (≤2.5 µm; PM 2.5), are breathable particles that easily can be deposited within the lungs, resulting in pulmonary and systemic inflammation. Although physical activity is strongly recommended, its effects when practiced in polluted environments are questionable. Therefore, the present study evaluated the pulmonary and systemic response of concomitant treadmill training with PM 2.5 and PM 10 exposure. Treadmill training inhibited PM 2.5- and PM 10-induced accumulation of total leukocytes (p<0.001), neutrophils (p<0.001), macrophages (p<0.001) and lymphocytes (p<0.001) in bronchoalveolar lavage (BAL), as well as the BAL levels of IL-1beta (p<0.001), CXCL1/KC (p<0.001) and TNF-alpha (p<0.001), whereas it increased IL-10 levels (p<0.05). Similar effects were observed on accumulation of polymorphonuclear (p<0.01) and mononuclear (p<0.01) cells in the lung parenchyma and in the peribronchial space. Treadmill training also inhibited PM 2.5- and PM 10-induced systemic inflammation, as observed in the number of total leukocytes (p<0.001) and in the plasma levels of IL-1beta (p<0.001), CXCL1/KC (p<0.001) and TNF-alpha (p<0.001), whereas it increased IL-10 levels (p<0.001). Treadmill training inhibits lung and systemic inflammation induced by particulate matter.

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