The impact of the pandemic caused by COVID-19 on air pollution in our cities is a proven fact, although its mechanisms are not known in great detail. The change in urban mobility patterns due to the restrictions imposed on the population during lockdown is a phenomenon that can be parameterized and studied from the perspective of spatial analysis. This study proposes an analysis of the guiding parameters of these changes from the perspective of spatial analysis. To do so, the case study of the city of Cartagena, a medium-sized city in Spain, has been analyzed throughout the period of mobility restrictions due to COVID-19. By means of a geostatistical analysis, changes in urban mobility patterns and the modal distribution of transport have been correlated with the evolution of environmental air quality indicators in the city. The results show that despite the positive effect of the pandemic in its beginnings on the environmental impact of urban mobility, the changes generated in the behavior patterns of current mobility users favor the most polluting modes of travel in cities.

Background Evidence of the impacts of ambient temperatures on emergency ambulance calls (EACs) in developing countries is rare. This study aimed to examine the impacts and burden of heat on EACs in China, quantify the contributions of regional modifiers and identify the vulnerable populations.

Methods A semi-parametric generalized additive model with a Poisson distribution was used to analyze the city-specific impacts of the daily maximum temperature (Tmax) on EACs during June to August in 2014-2017. Stratified analyses by sex and age were performed to identify vulnerable sub-populations. Meta-analysis was undertaken to illustrate the pooled associations. Further subgroup analysis stratified by climate, latitude and per capita disposable income (PCDI) and meta-regression analysis were conducted to explore the regional heterogeneity and quantify the contributions of possible modifiers. City- and region-specific attributable fractions (AF) of EACs attributable to heat were calculated.

Results Strong associations between daily Tmax and total EACs were observed in all cities. 11.7% (95% CI: 11.2%–12.3%) of EACs were attributed to high temperature in the ten Chinese cities, and the central region with lower PCDI had the highest AF of 17.8% (95% CI: 17.2%–18.4%). People living in the central region with lower PCDI, the people aged 18-44 years and 0-6years were identified to be more vulnerable to heat than others. The combined effects of PCDI, temperature and latitude contributed 88.56% to the regional heterogeneity.

Conclusions Our results complement the understanding of the burden of EACs attributable to heat in developing countries and the quantitative contribution of regional modifiers.