Air quality modelling in Catalonia from a combination of solar radiation, surface reflectance and elevation

The impact of data imputation on air quality prediction problem

With rising environmental concerns, accurate air quality predictions have become paramount as they help in planning preventive measures and policies for potential health hazards and environmental problems caused by poor air quality. Most of the time, air quality data are time series data. However, due to various reasons, we often encounter missing values in datasets collected during data preparation and aggregation steps. The inability to analyze and handle missing data will significantly hinder the data analysis process. To address this issue, this paper offers an extensive review of air quality prediction and missing data imputation techniques for time series, particularly in relation to environmental challenges. In addition, we empirically assess eight imputation methods, including mean, median, kNNI, MICE, SAITS, BRITS, MRNN, and Transformer, to scrutinize their impact on air quality data. The evaluation is conducted using diverse air quality datasets gathered from numerous cities globally. Based on these evaluations, we offer practical recommendations for practitioners dealing with missing data in time series scenarios for environmental data.

Impact of battery electric vehicle usage on air quality in three Chinese first-tier cities

China, the world leader in automobile production and sales, confronts the challenge of transportation emissions, which account for roughly 10% of its total carbon emissions. This study, utilizing real-world vehicle data from three major Chinese cities, assesses the impact of Battery Electric Vehicles (BEVs) on air quality. Our analysis reveals that BEVs, when replacing gasoline vehicles in their operational phase, significantly reduce emissions, with reductions ranging from 8.72 to 85.71 kg of CO2 per vehicle monthly. The average monthly reduction rate is 9.47%, though this effect is less pronounced during winter. Advanced BEVs, characterized by higher efficiency and newer technology, exhibit greater emission reduction benefits. While private BEVs generally contribute positively to environmental outcomes, taxi BEVs, due to their intensive usage patterns, show less environmental advantage and may sometimes worsen air quality. Looking ahead, we project substantial emission reductions from the replacement of gasoline vehicles with electric alternatives over the next decade. Policymakers are urged to adopt proactive measures, focusing on promoting medium to large electric vehicles and fostering the use of private and ride-hailing electric vehicles.

Machine learning algorithms to forecast air quality: a survey

Air pollution is a risk factor for many diseases that can lead to death. Therefore, it is important to develop forecasting mechanisms that can be used by the authorities, so that they can anticipate measures when high concentrations of certain pollutants are expected in the near future. Machine Learning models, in particular, Deep Learning models, have been widely used to forecast air quality. In this paper we present a comprehensive review of the main contributions in the field during the period 2011-2021. We have searched the main scientific publications databases and, after a careful selection, we have considered a total of 155 papers. The papers are classified in terms of geographical distribution, predicted values, predictor variables, evaluation metrics and Machine Learning model.

Connection between Weather Types and Air Pollution Levels: A 19-Year Study in Nine EMEP Stations in Spain

This study focuses on the analysis of the distribution, both spatial and temporal, of the PM10 (particulate matter with a diameter of 10 µm or less) concentrations recorded in nine EMEP (European Monitoring and Evaluation Programme) background stations distributed throughout mainland Spain between 2001 and 2019. A study of hierarchical clusters was used to classify the stations into three main groups with similarities in yearly concentrations: GC (coastal location), GNC (north-central location), and GSE (southeastern location). The highest PM10 concentrations were registered in summer. Annual evolution showed statistically significant decreasing trends in PM10 concentration in all the stations covering a range from -0.21 to -0.50 µg m-3/year for Barcarrota and Víznar, respectively. Through the Lamb classification, the weather types were defined during the study period, and those associated with high levels of pollution were identified. Finally, the values exceeding the limits established by the legislation were analyzed for every station assessed in the study.

Can new energy vehicles subsidy curb the urban air pollution? Empirical evidence from pilot cities in China

New energy vehicles (NEVs) are considered as the potential measure to address urban air pollution, and the Chinese government has launched a pilot subsidy scheme to improve its market penetration. We explored the environmental effects of subsidy on urban air pollution from the extensive and intensive margins and formulated a detailed panel dataset, covering 286 cities in China over the years 2006-2018. Moreover, the PSM-DID method and the instrumental strategy are used to confirm the robustness and validity of empirical results on the basis of comprehensive analysis of potential endogenous issues. The results indicate that the implementation of NEVs subsidy policy could significantly improve urban air quality in general, and as the subsidies scale increased by 1%, air pollution level will be reduced by about 0.15%. Then, from the perspective of the dynamic effect of subsidy, it not only has remarkable current environmental effect, but also an effective technology route in the long run. Simultaneously, compared with traditional intervention tools, subsidies enhance the diffusion of NEVs, reduce emissions of air pollutants while meeting residents' travel needs, and thus achieve incentive compatibility, which is the micro-foundation of environmental improvement. Nonetheless, we cannot simply believe the assertion that NEVs subsidy has positive environmental benefits to each region, as the non-clean generation of electricity in some areas will offset the potential environmental benefits. Additionally, the accelerated phase-out of NEVs subsidies at this stage may cause negative externalities of economy and environment, resulting the deadweight loss of industrial dividends accumulated in the early period. Based on above findings, governments should implement more flexible stimulus policies consistent with NEVs industry developments, rather than drastically reducing subsidies, while paying close attention to decarbonization of energy production stage.

Spatio-temporal air pollution modelling using a compositional approach

Air pollutant data are compositional in character because they describe quantitatively the parts of a whole (atmospheric composition). However, it is common to use air pollutant concentrations in statistical models without considering this characteristic of the data and, therefore, without control of common statistical problems, such as spurious correlations and subcompositional incoherence. This paper now proposes a daily multivariate spatio-temporal model with a compositional approach. The air pollution spatio-temporal model is based on a dynamic linear modelling framework with Bayesian inference. The novel modelling methodology was applied in an urban area for carbon monoxide (CO, mg·m-3), sulfur dioxide (SO2, μg·m-3), ozone (O3, μg·m-3), nitrogen dioxide (NO2, μg·m-3), and particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5, μg·m-3). The proposal complemented and improved the conventional approach in air pollution modelling. The main improvements come from a fast multivariate data description, high spatial-correlation, and adequate modelling of air pollutants with high variability.

The aggravation of urban air quality deterioration due to urbanization, transportation and economic development - Panel models with marginal effect analyses across China

In this paper, panel data models are established to examine the impacts of urban construction, transport facilities, and economic development on the urban air quality. Combined with data from different tiers of Chinese cities for two time series, 2010 and 2015, the variable-intercept model (VIM) is used to determine the parameters and significance of each independent variable. The marginal effects of different categories of independent variables (urbanization, transportation and economy) on the urban air quality are also studied with regard to the results of different VIMs. The results show that transportation factors (such as annual passenger trips, bus numbers and taxi numbers) have the most significant effects on the air quality for all the Chinese cities. Moreover, urbanized area and annual gross value of industrial output also have prominent impacts on the air quality across China. In addition, the marginal effects of the air quality index obtained via VIMs with classified local variables reflect that the influences of urbanization, transportation and economy on urban air quality are substantially different among different tiers of cities. Therefore, based on the findings, we propose measures to improve air quality for different tiers of cities, such as rational use of space resources, optimizing transport modes, and encouraging carpooling.