Comparative Study of Ambient Air Quality Prediction System Using Machine Learning to Predict Air Quality in Smart City

2020 ◽  
pp. 175-182 ◽  
Author(s):  
Gopal Sakarkar ◽  
Sofia Pillai ◽  
C. V. Rao ◽  
Atharva Peshkar ◽  
Shreyas Malewar
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Comparative Study ◽  
Smart City ◽  
Ambient Air ◽  
Quality Prediction ◽  
Prediction System ◽  
Ambient Air Quality ◽  
Air Quality Prediction

scholarly journals Research on air quality prediction method in Hangzhou based on machine learning

2021 ◽  
Vol 2010 (1) ◽  
pp. 012011
Author(s):  
Zhongjie Fu ◽  
Haiping Lin ◽  
Bingqiang Huang ◽  
Jiana Yao
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Prediction Method ◽  
Quality Prediction ◽  
Air Quality Prediction

scholarly journals Development of Korean Air Quality Prediction System version 1 (KAQPS v1) with focuses on practical issues

2020 ◽  
Vol 13 (3) ◽  
pp. 1055-1073 ◽  
Author(s):  
Kyunghwa Lee ◽  
Jinhyeok Yu ◽  
Sojin Lee ◽  
Mieun Park ◽  
Hun Hong ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Air Quality ◽  
Initial Conditions ◽  
Optimal Interpolation ◽  
Quality Prediction ◽  
Prediction System ◽  
Monitoring Networks ◽  
Alkyl Nitrates ◽  
Mixing Ratios ◽  
Air Quality Prediction

Abstract. For the purpose of providing reliable and robust air quality predictions, an air quality prediction system was developed for the main air quality criteria species in South Korea (PM10, PM2.5, CO, O3 and SO2). The main caveat of the system is to prepare the initial conditions (ICs) of the Community Multiscale Air Quality (CMAQ) model simulations using observations from the Geostationary Ocean Color Imager (GOCI) and ground-based monitoring networks in northeast Asia. The performance of the air quality prediction system was evaluated during the Korea-United States Air Quality Study (KORUS-AQ) campaign period (1 May–12 June 2016). Data assimilation (DA) of optimal interpolation (OI) with Kalman filter was used in this study. One major advantage of the system is that it can predict not only particulate matter (PM) concentrations but also PM chemical composition including five main constituents: sulfate (SO42-), nitrate (NO3-), ammonium (NH4+), organic aerosols (OAs) and elemental carbon (EC). In addition, it is also capable of predicting the concentrations of gaseous pollutants (CO, O3 and SO2). In this sense, this new air quality prediction system is comprehensive. The results with the ICs (DA RUN) were compared with those of the CMAQ simulations without ICs (BASE RUN). For almost all of the species, the application of ICs led to improved performance in terms of correlation, errors and biases over the entire campaign period. The DA RUN agreed reasonably well with the observations for PM10 (index of agreement IOA =0.60; mean bias MB =-13.54) and PM2.5 (IOA =0.71; MB =-2.43) as compared to the BASE RUN for PM10 (IOA =0.51; MB =-27.18) and PM2.5 (IOA =0.67; MB =-9.9). A significant improvement was also found with the DA RUN in terms of bias. For example, for CO, the MB of −0.27 (BASE RUN) was greatly enhanced to −0.036 (DA RUN). In the cases of O3 and SO2, the DA RUN also showed better performance than the BASE RUN. Further, several more practical issues frequently encountered in the air quality prediction system were also discussed. In order to attain more accurate ozone predictions, the DA of NO2 mixing ratios should be implemented with careful consideration of the measurement artifacts (i.e., inclusion of alkyl nitrates, HNO3 and peroxyacetyl nitrates – PANs – in the ground-observed NO2 mixing ratios). It was also discussed that, in order to ensure accurate nocturnal predictions of the concentrations of the ambient species, accurate predictions of the mixing layer heights (MLHs) should be achieved from the meteorological modeling. Several advantages of the current air quality prediction system, such as its non-static free-parameter scheme, dust episode prediction and possible multiple implementations of DA prior to actual predictions, were also discussed. These configurations are all possible because the current DA system is not computationally expensive. In the ongoing and future works, more advanced DA techniques such as the 3D variational (3DVAR) method and ensemble Kalman filter (EnK) are being tested and will be introduced to the Korean air quality prediction system (KAQPS).

scholarly journals Air Quality Prediction in Smart Cities Using Machine Learning Technologies based on Sensor Data: A Review

2020 ◽  
Vol 10 (7) ◽  
pp. 2401 ◽  
Author(s):  
Ditsuhi Iskandaryan ◽  
Francisco Ramos ◽  
Sergio Trilles
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Air Quality ◽  
Smart Cities ◽  
Learning Technologies ◽  
Sensor Data ◽  
Quality Prediction ◽  
Air Quality Prediction ◽  
Air Pollution Prediction ◽  
Pollution Prediction

The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features.

scholarly journals Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique

2019 ◽  
Vol 19 (17) ◽  
pp. 11303-11314 ◽  
Author(s):  
Tuan V. Vu ◽  
Zongbo Shi ◽  
Jing Cheng ◽  
Qiang Zhang ◽  
Kebin He ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Action Plan ◽  
Ambient Air ◽  
Meteorological Conditions ◽  
Assessment Process ◽  
Pollutant Emissions ◽  
Ambient Air Quality ◽  
Clean Air ◽  
The Impact

Abstract. A 5-year Clean Air Action Plan was implemented in 2013 to reduce air pollutant emissions and improve ambient air quality in Beijing. Assessment of this action plan is an essential part of the decision-making process to review its efficacy and to develop new policies. Both statistical and chemical transport modelling have been previously applied to assess the efficacy of this action plan. However, inherent uncertainties in these methods mean that new and independent methods are required to support the assessment process. Here, we applied a machine-learning-based random forest technique to quantify the effectiveness of Beijing's action plan by decoupling the impact of meteorology on ambient air quality. Our results demonstrate that meteorological conditions have an important impact on the year-to-year variations in ambient air quality. Further analyses show that the PM2.5 mass concentration would have broken the target of the plan (2017 annual PM2.5<60 µg m−3) were it not for the meteorological conditions in winter 2017 favouring the dispersion of air pollutants. However, over the whole period (2013–2017), the primary emission controls required by the action plan have led to significant reductions in PM2.5, PM10, NO2, SO2, and CO from 2013 to 2017 of approximately 34 %, 24 %, 17 %, 68 %, and 33 %, respectively, after meteorological correction. The marked decrease in PM2.5 and SO2 is largely attributable to a reduction in coal combustion. Our results indicate that the action plan has been highly effective in reducing the primary pollution emissions and improving air quality in Beijing. The action plan offers a successful example for developing air quality policies in other regions of China and other developing countries.

scholarly journals A Machine Learning Approach for Air Quality Prediction: Model Regularization and Optimization

2017 ◽  
Author(s):  
Dixian Zhu ◽  
Changjie Cai ◽  
Tianbao Yang ◽  
Xun Zhou
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Air Quality ◽  
Regression Models ◽  
Small Scale ◽  
Quality Prediction ◽  
Pollution Concentration ◽  
Task Learning ◽  
Air Quality Prediction ◽  
Standard Regression

In this paper, we tackle air quality forecasting by using machine learning approaches to predict the hourly concentration of air pollutants (e.g., Ozone, PM2.5 and Sulfur Dioxide). Machine learning, as one of the most popular techniques, is able to efficiently train a model on big data by using large-scale optimization algorithms. Although there exists some works applying machine learning to air quality prediction, most of the prior studies are restricted to small scale data and simply train standard regression models (linear or non-linear) to predict the hourly air pollution concentration. In this work, we propose refined models to predict the hourly air pollution concentration based on meteorological data of previous days by formulating the prediction of 24 hours as a multi-task learning problem. It enables us to select a good model with different regularization techniques. We propose a useful regularization by enforcing the prediction models of consecutive hours to be close to each other, and compare with several typical regularizations for multi-task learning including standard Frobenius norm regularization, nuclear norm regularization, ℓ2,1 norm regularization. Our experiments show the proposed formulations and regularization achieve better performance than existing standard regression models and existing regularizations.

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