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

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

Assessing the effects of trans-boundary aerosol transport between various city clusters on regional haze episodes in spring over East China

Regional haze episodes have been frequently reported in east China since 2000. In the present study, two regional haze episodes over east China in the spring of 2011 were examined by observations and simulations conducted by a three-dimensional regional chemical transport model (NAQPMS) with an on-line tracer-tagged module. The model reproduced accurately the observed PM2.5 with correlation coefficient ranging from 0.52 to 0.76 and root mean square error (RMSE) of 20–50µg/m3 in four city clusters (Yangtze River Delta, Shandong Peninsula, Huabei Plain and Central Liaoning) over east China. Our results indicate that a northward cross-border transport from the Yangtze River Delta to Central Liaoning below 2 km above ground played an important role in the formation of these regional high PM2.5 episodes. Contributions of regional transport from outside city clusters presented an increasing trend from south to north. In the northernmost cluster (Central Liaoning), the contribution from other city clusters reached 40–50% during the two episodes. In contrast, it was below 10% in the Yangtze River Delta (southernmost cluster). Mixing accumulation of pollutants from various city clusters during transport was responsible for this trend. Furthermore, a preliminary estimate shows that cross-border transport of PM2.5 might increase 0.5–3% daily mortality during the high PM2.5 episodes

The relationships between PM2.5 and meteorological factors in China: Seasonal and regional variations

The interactions between PM2.5 and meteorological factors play a crucial role in air pollution analysis. However, previous studies that have researched the relationships between PM2.5 concentration and meteorological conditions have been mainly confined to a certain city or district, and the correlation over the whole of China remains unclear. Whether or not spatial and seasonal variations exit deserves further research. In this study, the relationships between PM2.5 concentration and meteorological factors were investigated in 74 major cities in China for a continuous period of 22 months from February 2013 to November 2014, at season, year, city, and regional scales, and the spatial and seasonal variations were analyzed. The meteorological factors were relative humidity (RH), temperature (TEM), wind speed (WS), and surface pressure (PS). We found that spatial and seasonal variations of their relationships with PM2.5 do exist. Spatially, RH is positively correlated with PM2.5 concentration in North China and Urumqi, but the relationship turns to negative in other areas of China. WS is negatively correlated with PM2.5 everywhere expect for Hainan Island. PS has a strong positive relationship with PM2.5 concentration in Northeast China and Mid-south China, and in other areas the correlation is weak. Seasonally, the positive correlation between PM2.5 concentration and RH is stronger in winter and spring. TEM has a negative relationship with PM2.5 in autumn and the opposite in winter. PS is more positively correlated with PM2.5 in autumn than in other seasons. Our study investigated the relationships between PM2.5 and meteorological factors in terms of spatial and seasonal variations, and the conclusions about the relationships between PM2.5 and meteorological factors are more comprehensive and precise than before.Comment: 3 tables, 13 figure

A Modeling Study of PM2.5 Air Pollution in China: Primary and Secondary Inorganic Aerosols

Quantitative information on sources and source region contributions to particulate matter (PM) concentration in China is currently poorly understood but is urgently needed to make emission control strategies. In this study, source-oriented Community Multi-scale Air Quality (CMAQ) models are used to study the formation of and source contributions to primary and secondary PM in China. The results show that inter-regional transport of sulfate, nitrate and ammonium ion (SNA) occurs frequently, especially in the winter. The emissions from non-local regional can contribute 30-70% of the total SNA in different regions and seasons. It is also found that surface heterogeneous reactions of NO2 and SO2 and higher emissions of NH3 are needed to better reproduce the observed high concentrations of SNA in Beijing, and potentially in other areas. Residential sources account for significant fractions (19%-68% in Beijing and 6%-30% in Shanghai) of primary PM2.5, with higher contributions occur in winter. Industrial emissions are important throughout the year (15%-45% in Beijing and 39%-60% in Shanghai). Dust contributions can be as much as 20-30% in spring and fall seasons. Contributions to primary PM2.5 from other sources are relatively small. In Shanghai, local emissions account for 70-90 % of primary PM2.5. However, local emissions only contribute to 45%-55% of primary PM2.5 in Beijing. These suggest that inter-regional emission control strategies are necessary to reduce PM pollution in China. Source and source region contributions to primary PM2.5 components are determined using a novel multi-linear regression technique that combines the observation data and the source-oriented model predictions of primary PM2.5 mass concentrations

Severe Air Pollution Events Not Avoided By Reduced Anthropogenic Activities During Covid-19 Outbreak

Due to the pandemic of coronavirus disease 2019 in China, almost all avoidable activities in China are prohibited since Wuhan announced lockdown on January 23, 2020. With reduced activities, severe air pollution events still occurred in the North China Plain, causing discussions regarding why severe air pollution was not avoided. The Community Multi-scale Air Quality model was applied during January 01 to February 12, 2020 to study PM2.5 changes under emission reduction scenarios. The estimated emission reduction case (Case 3) better reproduced PM2.5. Compared with the case without emission change (Case 1), Case 3 predicted that PM2.5 concentrations decreased by up to 20% with absolute decreases of 5.35, 6.37, 9.23, 10.25, 10.30, 12.14, 12.75, 14.41, 18.00 and 30.79 mu g/m(3) in Guangzhou, Shanghai, Beijing, Shijiazhuang, Tianjin, Jinan, Taiyuan,)(fan, Zhengzhou, Wuhan, respectively. In high-pollution days with PM2.5 greater than 75 mu g/m(3), the reductions of PM2.5 in Case 3 were 7.78, 9.51, 11.38, 13.42, 13.64, 14.15, 14.42, 16.95 and 22.08 mu g/m(3) in Shanghai, Jinan, Shijiazhuang, Beijing, Taiyuan, Xi\u27an, Tianjin, Zhengzhou and Wuhan, respectively. The reductions in emissions of PM2.5 precursors were 2 times of that in concentrations, indicating that meteorology was unfavorable during simulation episode. A further analysis shows that benefits of emission reductions were overwhelmed by adverse meteorology and severe air pollution events were not avoided. This study highlights that large emissions reduction in transportation and slight reduction in industrial would not help avoid severe air pollution in China, especially when meteorology is unfavorable. More efforts should be made to completely avoid severe air pollution

Air Pollution Meteorology

Although air pollution is usually linked with human activities, natural processes may also lead to major concentrations of hazardous substances in the low atmosphere. Pollutant levels may be reduced when emissions can be controlled. However, the impact of meteorological variables on the concentrations measured may be noticeable, and these variables cannot be controlled. This book is devoted to the influence of meteorological processes on the pollutant concentrations recorded in the low atmosphere. Measurements, cycles, statistical procedures, as well as specific variables such as the synoptic pattern, temperature inversion, or the calculation of back-trajectories, are considered in the studies included in this book to highlight the relationship between air pollution and meteorological variables. In addition, the state of the art of this subject following meteorological scales, from micro to macro-scale, is presented. Consequently, this book focuses on applied science and seeks to further current knowledge of what contribution meteorological processes make to the concentrations measured in order to achieve greater control over air pollution

Observed coupling between air mass history, secondary growth of nucleation mode particles and aerosol pollution levels in Beijing

Atmospheric aerosols have significant effects on the climate and on human health. New particle formation (NPF) is globally an important source of aerosols but its relevance especially towards aerosol mass loadings in highly polluted regions is still controversial. In addition, uncertainties remain regarding the processes leading to severe pollution episodes, concerning e.g. the role of atmospheric transport. In this study, we utilize air mass history analysis in combination with different fields related to the intensity of anthropogenic emissions in order to calculate air mass exposure to anthropogenic emissions (AME) prior to their arrival at Beijing, China. The AME is used as a semi-quantitative metric for describing the effect of air mass history on the potential for aerosol formation. We show that NPF events occur in clean air masses, described by low AME. However, increasing AME seems to be required for substantial growth of nucleation mode (diameter < 30 nm) particles, originating either from NPF or direct emissions, into larger mass-relevant sizes. This finding assists in establishing and understanding the connection between small nucleation mode particles, secondary aerosol formation and the development of pollution episodes. We further use the AME, in combination with basic meteorological variables, for developing a simple and easy-to-apply regression model to predict aerosol volume and mass concentrations. Since the model directly only accounts for changes in meteorological conditions, it can also be used to estimate the influence of emission changes on pollution levels. We apply the developed model to briefly investigate the effects of the COVID-19 lockdown on PM2.5 concentrations in Beijing. While no clear influence directly attributable to the lockdown measures is found, the results are in line with other studies utilizing more widely applied approaches.Peer reviewe

A 3D study on the amplification of regional haze and particle growth by local emissions

The role of new particle formation (NPF) events and their contribution to haze formation through subsequent growth in polluted megacities is still controversial. To improve the understanding of the sources, meteorological conditions, and chemistry behind air pollution, we performed simultaneous measurements of aerosol composition and particle number size distributions at ground level and at 260 m in central Beijing, China, during a total of 4 months in 2015-2017. Our measurements show a pronounced decoupling of gas-to-particle conversion between the two heights, leading to different haze processes in terms of particle size distributions and chemical compositions. The development of haze was initiated by the growth of freshly formed particles at both heights, whereas the more severe haze at ground level was connected directly to local primary particles and gaseous precursors leading to higher particle growth rates. The particle growth creates a feedback loop, in which a further development of haze increases the atmospheric stability, which in turn strengthens the persisting apparent decoupling between the two heights and increases the severity of haze at ground level. Moreover, we complemented our field observations with model analyses, which suggest that the growth of NPF-originated particles accounted up to similar to 60% of the accumulation mode particles in the Beijing-Tianjin-Hebei area during haze conditions. The results suggest that a reduction in anthropogenic gaseous precursors, suppressing particle growth, is a critical step for alleviating haze although the number concentration of freshly formed particles (3-40 nm) via NPF does not reduce after emission controls.Peer reviewe

City-level air quality improvement in the Beijing-Tianjin-Hebei region from 2016/17 to 2017/18 heating seasons: Attributions and process analysis

With the implementation of clean air strategies, PM_(2.5) pollution abatement has been observed in the “2 + 26” cities in the Beijing-Tianjin-Hebei (BTH) region (referred to as the BTH2+26) and their surrounding areas. To identify the drivers for PM_(2.5) concentration decreases in the BTH2+26 cites from the 2016/17 heating season (HS1617) to the 2017/18 heating season (HS1718), we investigated the contributions of meteorological conditions and emission-reduction measures by Community Multi-Scale Air Quality (CMAQ) model simulations. The source apportionments of five sector sources (i.e., agriculture, industry, power plants, traffic and residential), and regional sources (i.e., local, within-BTH: other cities within the BTH2+26 cities, outside-BTH, and boundary conditions (BCON)) to the PM_(2.5) decreases in the BTH2+26 cities were estimated with the Integrated Source Apportionment Method (ISAM). Mean PM_(2.5) concentrations in the BTH2+26 cities substantially decreased from 77.4 to 152.5 μg m⁻³ in HS1617 to 52.9–101.9 μg m⁻³ in HS1718, with the numbers of heavy haze (daily PM_(2.5) ≥150 μg m⁻³) days decreasing from 17-77 to 5–30 days. The model simulation results indicated that the PM_(2.5) concentration decreases in most of the BTH2+26 cities were attributed to emission reductions (0.4–55.0 μg m⁻³, 2.3–81.6% of total), but the favorable meteorological conditions also played important roles (1.9–25.4 μg m⁻³, 18.4–97.7%). Residential sources dominated the PM_(2.5) reductions, leading to decreases in average PM_(2.5) concentrations by more than 30 μg m⁻³ in severely polluted cities (i.e., Shijiazhuang, Baoding, Xingtai, and Beijing). Regional source analyses showed that both local and within-BTH sources were significant contributors to PM_(2.5) concentrations for most cities. Emission controls in local and within-BTH sources in HS1718 decreased the average PM_(2.5) concentrations by 0.1–47.2 μg m⁻³ and 0.3–22.1 μg m⁻³, respectively, relative to those in HS1617. Here we demonstrate that a combination of favorable meteorological conditions and anthropogenic emission reductions contributed to the improvement of air quality from HS1617 to HS1718 in the BTH2+26 cities