Due to recent developments in the global economy, transportation, and industrialization, air pollution is one of main environmental issues in the 21st century. The current study aimed to predict both short-term and long-term air pollution in Jiangsu Province, China, based on the Prophet forecasting model (PFM). We collected data from 72 air quality monitoring stations to forecast six air pollutants: PM₁₀, PM_2.5, SO₂, NO₂, CO, and O₃. To determine the accuracy of the model and to compare its results with predicted and actual values, we used the correlation coefficient (R), mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). The results show that PFM predicted PM₁₀ and PM_2.5 with R values of 0.40 and 0.52, RMSE values of 16.37 and 12.07 μg/m³, and MAE values of 11.74 and 8.22 μg/m³, respectively. Among other pollutants, PFM also predicted SO₂, NO₂, CO, and O₃ with R values are between 5 μg/m³ to 12 μg/m³; and MAE values between 2 μg/m³ to 11 μg/m³. PFM has extensive power to accurately predict the concentrations of air pollutants and can be used to forecast air pollution in other regions. The results of this research will be helpful for local authorities and policymakers to control air pollution and plan accordingly in upcoming years.

It has been two decades since virtual geographic environments (VGEs) were initially proposed. While relevant theories and technologies are evolving, data organization models have always been the foundation of VGE development, and they require further exploration. Based on the comprehensive consideration of the characteristics of VGEs, geographic scene is proposed to organize geographic information and data. We empirically find that geographic scene provides a suitable organization schema to support geo-visualization, geo-simulation, and geo-collaboration. To systematically investigate the concept and method of geographic scene, Geographic Scenario is proposed as the theory on developing geographic scene, and corresponding key issues of the Geographic Scenario are illustrated in this article. Prospects of the proposed method are discussed with the hope of informing future studies of VGEs.

A unique illness, the coronavirus disease 2019 (COVID-19), emerged in Wuhan, People’s Republic of China, in December 2019. To reduce the spread of the virus, strict lockdown policies and control measures were put in place all over the world. Due to these enforced limitations, a drastic drop in air pollution and an improvement in air quality were observed. The present study used six air pollutants (PM₁₀, PM_2.5, SO₂, NO₂, CO and O₃) to observe trends before, during and after the COVID-19 lockdown period in Nanjing, China. The data were divided into six phases: P1–P3, pre-lockdown (1 October–31 December 2019), lockdown (1 January–31 March 2020), after lockdown (1 April–30 June 2020), P4–P6: the same dates as the lockdown but during 2017, 2018 and 2019. The results indicate that compared with the pre-lockdown phase, the PM₁₀ and PM_2.5 average concentrations decreased by –27.71% and –5.09%. Compared with the previous three years, 2017–2019, the reductions in PM₁₀ and PM_2.5 were –37.99% and –33.56%, respectively. Among other pollutants, concentrations of SO₂ (–32.90%), NO₂ (–34.66%) and CO (–16.85%) also decreased during the lockdown, while the concentration of O₃ increased by approximately 25.45%. Moreover, compared with the pre- and during lockdown phases, PM₁₀, PM_2.5 and NO₂ showed decreasing trends while SO₂, CO and O₃ concentrations increased. These findings present a road map for upcoming studies and provide a new path for policymakers to create policies to improve air quality.

Urban street comfort is a crucial measure of street environmental quality. However, traditional evaluations primarily focus on physical elements, often neglecting pedestrian perceptions. In this study, considering five core evaluation dimensions—safety, mobility, aesthetics, perceptibility, and convenience—an innovative quantitative evaluation model is proposed to assess pedestrian-perceived comfort on urban streets by integrating physical environmental factors and subjective experiences. This analysis comprises two steps: evaluation indicator extraction and weight application. Indicators are extracted from multi-source data (street-view images, real-time traffic data, points of interest, and pedestrian surveys) using a deep learning method. A comprehensive weighting method combining entropy weight and the analytic hierarchy process is used to determine the relative importance of each factor. This study focuses on Nanjing as a case study, and the results reveal significant variations across the five dimensions and their 11 secondary indicators. Street environment safety (0.143) is critical for street safety, while the degree of street traffic congestion (0.121) dominates street mobility. Street aesthetics is primarily influenced by building enclosure (0.105), and street convenience is strongly affected by the number of surrounding bus stops (0.260). Spatial analysis indicates higher comfort levels in urban centers due to well-developed infrastructure, whereas peripheral areas face challenges from inadequate facilities. Notably, areas around parks demonstrate elevated pedestrian-perceived comfort levels, highlighting the importance of green spaces. Overall, the proposed evaluation system provides new insights from the perspective of pedestrian experience and offers valuable guidance for urban planning and policy.