Impact of urban morphology on the spatial and temporal distribution of PM2.5 concentration: A numerical simulation with WRF/CMAQ model in Wuhan, China

PM2.5 concentration prediction algorithm integrating traffic congestion index

In this study, a strategy is proposed to use the congestion index as a new input feature. This approach can reveal more deeply the complex effects of traffic conditions on variations in particulate matter (PM2.5) concentrations. To assess the effectiveness of this strategy, we conducted an ablation experiment on the congestion index and implemented a multi-scale input model. Compared with conventional models, the strategy reduces the root mean square error (RMSE) of all benchmark models by > 6.07 % on average, and the best-performing model reduces it by 12.06 %, demonstrating excellent performance improvement. In addition, even with high traffic emissions, the RMSE during peak hours is still below 9.83 µg/m3, which proves the effectiveness of the strategy by effectively addressing pollution hotspots. This study provides new ideas for improving urban environmental quality and public health and anticipates inspiring further research in this domain.

Urban form regulation for synergetic PM2.5 and O3 control: A multi-indicator constrained DNN simulation

Air quality optimization simulation incorporating urban form indicators (UFIs) is an effective approach for air quality-friendly urban planning and long-term air quality improvement. However, the development of synergetic multi-pollutant control techniques considering UFIs is still in early stages due to significant spatiotemporal variations among pollutants and complex interplay with UFIs. To address this, a novel Urban Form Regulation-Aided Air Quality Optimization Model (UFR-AQOM) was proposed. The model established a nonlinear mapping relationship between PM2.5, O3 concentrations and UFIs using a deep residual network based on autoencoders. It employed an inequality constraint strategy to regulate UFIs for PM2.5 and O3 optimization. Two sets of comparative experiments were conducted in mainland China: PM2.5 attainment simulation (Scenario 1) and collaborative attainment simulation of PM2.5 and O3 (Scenario 2). Results demonstrated the model's high accuracy, with validation R2 values of 0.97 for both PM2.5 and O3, and validation RMSE values of 1.41 μg/m3 and 2.18 μg/m3, respectively. Scenario 2 outperformed Scenario 1 by reducing O3 while maintaining PM2.5 optimization. Additionally, regulation effectiveness variations across different regions and a higher sensitivity of PM2.5 to changes in UFIs compared to O3 were observed. For effective synergetic control of PM2.5 and O3, it is recommended to enhance overall regulation of forest land area, parks coverage, patch density, largest patch index, and road density, while implementing zoned regulation of the proportion of water bodies, industrial area, residential area in regions. Findings can support the urban planning for synergetic PM2.5 and O3 control.

A deep learning-based hybrid method for PM2.5 prediction in central and western China

To mitigate the adverse effects of air pollution, accurate PM2.5 prediction is particularly important. It is difficult for existing models to escape the limitations attached to a single model itself. This study proposes a hybrid PM2.5 prediction model utilizing deep learning techniques, which aims to complement each other's strengths through model fusion. The model integrates the transformer and LSTM architectures and employs parameter optimization through the particle swarm optimization (PSO) algorithm. The proposed model achieves superior performance by utilizing the gating mechanism of the LSTM model, the positional encoding and self-attention mechanism of the Transformer model, and PSO's robust optimization capabilities. Experimental results show that the new model outperforms both the traditional LSTM model and the PSO-LSTM model in the PM2.5 prediction task, and its evaluation metrics, R2, MAE, MBE, RMSE, and MAPE, are all improved. Furthermore, the model demonstrates stable performance across different cities and various periods. This study offers a robust approach to improving the accuracy and reliability of PM2.5 forecasting.

Impact of urban space on PM2.5 distribution: A multiscale and seasonal study in the Yangtze River Delta urban agglomeration

Despite concerted efforts in emission control, air pollution control remains challenging. Urban planning has emerged as a crucial strategy for mitigating PM2.5 pollution. What remains unclear is the impact of urban form and their interactions with seasonal changes. In this study, base on the air quality monitoring stations in the Yangtze River Delta urban agglomeration, the relationship between urban spatial indicators (building morphology and land use) and PM2.5 concentrations was investigated using full subset regression and variance partitioning analysis, and seasonal differences were further analysed. Our findings reveal that PM2.5 pollution exhibits different sensitivities to spatial scales, with higher sensitivity to the local microclimate formed by the three-dimensional structure of buildings at the local scale, while land use exerts greater influence at larger scales. Specifically, land use indicators contributed sustantially more to the PM2.5 prediction model as buffer zone expand (from an average of 2.41% at 100 m range to 47.30% at 5000 m range), whereas building morphology indicators display an inverse trend (from an average of 13.84% at 100 m range to 1.88% at 5000 m range). These results enderscore the importance of considering building morphology in local-scale urban planning, where the increasing building height can significantly enhance the disperion of PM2.5 pollution. Conversely, large-scale urban planning should prioritize the mixed use of green spaces and construction lands to mitigate PM2.5 pollution. Moreover, the significant seasonal differences in the ralationship between urban spatical indicatiors and PM2.5 pollution were observed. Particularly moteworthy is the heightened association between forest, water indicators and PM2.5 concentrations in summer, indicating the urban forests may facilitate the formation of volatile compunds, exacerbating the PM2.5 pollution. Our study provides a theoretical basis for addressing scale-related challenges in urban spatial planning, thereby forstering the sustainable development of cities.

A hybrid multi-scale fusion paradigm for AQI prediction based on the secondary decomposition

With rapid industrialization and urbanization, air pollution has become an increasingly severe problem. As a key indicator of air quality, accurate prediction of the air quality index (AQI) is essential for policymakers to establish effective early warning management mechanisms and adjust living plans. In this work, a hybrid multi-scale fusion prediction paradigm is proposed for the complex AQI time series prediction. First, an initial decomposition and integration of the original data is performed by combining the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and sample entropy (SE). Then, the subsequences, divided into high-frequency and low-frequency groups, are applied to different processing methods. Among them, the variational mode decomposition (VMD) is chosen to perform a secondary decomposition of the high-frequency sequence groups and integrated by using K-means clustering with sample entropy. Finally, multi-scale fusion training of sequence prediction results with different frequencies by using long short-term memory (LSTM) yields more accurate results with R2 of 0.9715, RMSE of 2.0327, MAE of 0.0154, and MAPE of 0.0488. Furthermore, validation of the AQI datasets acquired from four different cities demonstrates that the new paradigm is more robust and generalizable as compared to other baseline methods. Therefore, this model not only holds potential value in developing AQI prediction models but also serves as a valuable reference for future research on AQI control strategies.

How do taxi drivers expose to fine particulate matter (PM2.5) in a Chinese megacity: a rapid assessment incorporating with satellite-derived information and urban mobility data

BACKGROUND

Taxi drivers in a Chinese megacity are frequently exposed to traffic-related particulate matter (PM2.5) due to their job nature, busy road traffic, and urban density. A robust method to quantify dynamic population exposure to PM2.5 among taxi drivers is important for occupational risk prevention, however, it is limited by data availability.

METHODS

This study proposed a rapid assessment of dynamic exposure to PM2.5 among drivers based on satellite-derived information, air quality data from monitoring stations, and GPS-based taxi trajectory data. An empirical study was conducted in Wuhan, China, to examine spatial and temporal variability of dynamic exposure and compare whether drivers' exposure exceeded the World Health Organization (WHO) and China air quality guideline thresholds. Kernel density estimation was conducted to further explore the relationship between dynamic exposure and taxi drivers' activities.

RESULTS

The taxi drivers' weekday and weekend 24-h PM2.5 exposure was 83.60 μg/m3 and 55.62 μg/m3 respectively, 3.4 and 2.2 times than the WHO's recommended level of 25 µg/m3. Specifically, drivers with high PM2.5 exposure had a higher average trip distance and smaller activity areas. Although major transportation interchanges/terminals were the common activity hotspots for both taxi drivers with high and low exposure, activity hotspots of drivers with high exposure were mainly located in busy riverside commercial areas within historic and central districts bounded by the "Inner Ring Road", while hotspots of drivers with low exposure were new commercial areas in the extended urbanized area bounded by the "Third Ring Road".

CONCLUSION

These findings emphasized the need for air quality management and community planning to mitigate the potential health risks of taxi drivers.

Large contributions of emission reductions and meteorological conditions to the abatement of PM2.5 in Beijing during the 24th Winter Olympic Games in 2022

To guarantee the blue skies for the 2022 Winter Olympics held in Beijing and Zhangjiakou from February 4 to 20, Beijing and its surrounding areas adopted a series of emission control measures. This provides an opportunity to determine the impacts of large-scale temporary control measures on the air quality in Beijing during this special period. Here, we applied the WRF-CMAQ model to quantify the contributions of emission reduction measures and meteorological conditions. Results show that meteorological conditions in 2022 decreased PM2.5 in Beijing by 6.9 and 11.8 µg/m3 relative to 2021 under the scenarios with and without emission reductions, respectively. Strict emission reduction measures implemented in Beijing and seven neighboring provinces resulted in an average decrease of 13.0 µg/m3 (-41.2%) in PM2.5 in Beijing. Over the entire period, local emission reductions contributed more to good air quality in Beijing than nonlocal emission reductions. Under the emission reduction scenario, local, controlled regions, other regions, and boundary conditions contributed 47.7%, 42.0%, 5.3%, and 5.0% to the PM2.5 concentrations in Beijing, respectively. The results indicate that during the cleaning period with the air masses from the northwest, the abatements of PM2.5 were mainly caused by local emission reductions. However, during the potential pollution period with the air masses from the east-northeast and west-southwest, the abatements of PM2.5 were caused by both local and nonlocal emission reductions almost equally. This implies that regional coordinated prevention and control strategies need to be arranged scientifically and rationally when heavy pollution events are forecasted.

Assessing the impact of urban form and urbanization process on tropospheric nitrogen dioxide pollution in the Yangtze River Delta, China

Optimizing urban form through urban planning and management policies can improve air quality and transition to demand-side control. Nitrogen dioxide (NO2) in the urban atmosphere, mainly emitted by anthropogenic sources such as industry and vehicles, is a key precursor of fine particles and ozone pollution. Both NO2 and its secondary pollutants pose health risks for humans. Here we assess the interactions between urban forms and airborne NO2 pollution in different cities with various stages of urbanization in the Yangtze River Delta (YRD) in China, by using the machine learning and geographical regression model. The results reveal a strong correlation between urban fragmentation and tropospheric NO2 vertical column density (TVCD) in YRD cities in 2020, particularly those with lower or higher levels of urbanization. The correlation coefficients (R2) between NO2 TVCD and the largest patch index (a metric of urban fragmentation) in different cities are greater than 0.8. For cities at other urbanization stages, population and road density are strongly correlated with NO2 TVCD, with an R2 larger than 0.61. This study highlights the interdependence among urbanization, urban forms, and air pollution, emphasizing the importance of customized urban landscape management strategies for mitigating urban air pollution.

Performance evaluation and users' perception of courtyards role in indoor areas of mediterranean social housing

Cities confront two critical challenges: general overheating and inefficient use of energy resources within their housing buildings, both adversely affecting urban citizens' daily lives. To mitigate these issues, passive techniques offer promising solutions on enhancing building comfort levels from a sustainable approach. Although this energy efficiency of air-conditioning systems in buildings in warm climates has been extensively analysed, the influence of the microclimate of transitional spaces attached to them on this performance has not yet been properly assessed. Investigating the potential benefits of the implementation of courtyards within Seville's social housing infrastructure for passive conditioning purposes is one way of reducing this research gap. Furthermore, the study also includes the subjective perception of users' thermal well-being around these spaces and their own social relationship related to their use. The work relies on detailed data analyses carried out using DesignBuilder software to quantify the benefit effectively accrued from courtyard utilization. Concurrently, user surveys conducted help determine perceived thermal comfort aiding better configuration management and passive design strategies of urban social housing. Findings from monitoring and simulation reveal that courtyards work faultlessly as a highly effective and efficient passive cooling system whilst promoting energy efficiency up to 20,5%. Surveys confirmed these findings with data revealing significant improvements in thermal comforts perception inside courtyards and within indoor spaces adjacent to the courtyards. This research provides novel insights into how architects and urban managers might integrate passive strategies into future designs for optimizing comfort levels in social housing using courtyards as one possible environmental measure for achieving sustainability targets.

Dependence Analysis of PM2.5 Concentrations in 295 Chinese Cities in the Winter of 2019–2020

Considering the current severe atmospheric pollution problems in China, a comprehensive understanding of the distribution and spatial variability of PM2.5 is critically important for controlling pollution and improving the future atmospheric environment. This study first explored the distribution of PM2.5 concentrations in China, and then developed a methodology of “dependence analysis” to investigate the relationship of PM2.5 in different cities in China. The data of daily PM2.5 concentrations were collected from the environmental monitoring stations in 295 cities in China. This study also developed a set of procedures to evaluate the spatial dependence of PM2.5 among the 295 Chinese cities. The results showed that there was a total of 154 city pairs with dependence type “11”, under a significance level of 0.5%. Dependence type “11” mainly occurred between nearby cities, and the distance between 89.0% of the dependent city pairs was less than 200 km. Furthermore, the dependent pairs mainly clustered in the North China Plain, the Northeast Plain, the Middle and Lower Yangtze Plain and the Fen-Wei Plain. The geographic conditions of the Plain areas were more conducive to the spread of PM2.5 contaminants, while the mountain topography was unfavorable for the formation of PM2.5 dependencies. The dependent city couples with distances greater than 200 km were all located within the Plain areas. The high concentration of PM2.5 did not necessarily lead to PM2.5 dependences between city pairs. The methodology and models developed in this study will help explain the concentration distributions and spatial dependence of the main atmospheric pollutants in China, providing guidance for the prevention of large-scale air pollution, and the improvement of the future atmospheric environment.

Consistency of the relationship between air pollution and the urban form: Evidence from the COVID-19 natural experiment

The lockdown measures enacted to control the COVID-19 pandemic in Wuhan, China, resulted in a suspension of nearly all non-essential human activities on January 23, 2020. Nevertheless, the lockdown provided a natural experiment to understand the consistency of the relationship between the urban form and air pollution with different compositions of locally or regionally transported sources. This study investigated the variations in six air pollutants (PM2.5, PM10, NO2, CO, O3, and SO2) in Wuhan before and during the lockdown and in the two same time spans in 2021. Moreover, a hierarchical agglomerative cluster analysis was conducted to differentiate the relative levels of pollutants and to detect the relationships between the air pollutants and the urban form during these four periods. Several features depicting the urban physical structures delivered consistent impacts. A lower building density and plot ratio, and a higher porosity always mitigated the concentrations of NO2 and PM2.5. However, they had inverse effects on O3 during the non-lockdown periods. PM10, CO, and SO2 concentrations have little correlation with the urban form. This study improves the comprehensive understanding of the effect of the urban form on ambient air pollution and suggests practical strategies for mitigating air pollution in Wuhan.

Effects of the joint prevention and control of atmospheric pollution policy on air pollutants-A quantitative analysis of Chinese policy texts

Joint prevention and control of atmospheric pollution (JPCAP) policies play a vital role in alleviating regional pollution. Based on Latent Dirichlet Allocation (LDA) model, we construct two policy strength measures of effectiveness and number, and investigate the effects of policy strength on air pollutant emissions for four types of JPCAP policies. The results show that the effects of economic incentive policy tools and supporting policy tools on emission reduction deviate significantly from policy preferences. Economic incentive policy tools are the most effective in promoting emission reductions in SO₂, NOx and dust, but their effectiveness are the lowest in reality. Supporting policy tools, with the highest strength, have little effect on emission reduction. Command-control policies and persuasion policies are both relatively high in quantity and effectiveness. In addition, policy strength plays a more important role in reducing air pollutants in key regions than in non-key regions. JPCAP policies have gradually changed from a single policy tool to multiple policy tools, and the government shifted its attention to improving the legal effectiveness of policies after 2015. Finally, we propose some policy implications to optimize JPCAP policies and address regional air pollution problem.

Variations in Nocturnal Residual Layer Height and Its Effects on Surface PM2.5 over Wuhan, China

Large amounts of aerosols remain in the residual layer (RL) after sunset, which may be the source of the next day’s pollutants. However, the characteristics of the nocturnal residual layer height (RLH) and its effect on urban environment pollution are unknown. In this study, the characteristics of the RLH and its effect on fine particles with diameters <2.5 μm (PM2.5) were investigated using lidar data from January 2017 to December 2019. The results show that the RLH is highest in summer (1.55 ± 0.55 km), followed by spring (1.40 ± 0.58 km) and autumn (1.26 ± 0.47 km), and is lowest in winter (1.11 ± 0.44 km). The effect of surface meteorological factors on the RLH were also studied. The correlation coefficients (R) between the RLH and the temperature, relative humidity, wind speed, and pressure were 0.38, −0.18, 0.15, and −0.36, respectively. The results indicate that the surface meteorological parameters exhibit a slight correlation with the RLH, but the high relative humidity was accompanied by a low RLH and high PM2.5 concentrations. Finally, the influence of the RLH on PM2.5 was discussed under different aerosol-loading periods. The aerosol optical depth (AOD) was employed to represent the total amount of pollutants. The results show that the RLH has an effect on PM2.5 when the AOD is small but has almost no effect on PM2.5 when the AOD is high. In addition, the R between the nighttime mean RLH and the following daytime PM2.5 at low AOD is −0.49, suggesting that the RLH may affect the following daytime surface PM2.5. The results of this study have a guiding significance for understanding the interaction between aerosols and the boundary layer.