Rapid motorization poses challenges to transport decarbonization and air quality in China. The built environment shapes travel behavior, and transit-oriented development (TOD) has emerged as a key strategy for low-carbon mobility. This study develops a predictive model that links built environment indicators to travel modal shares and associated emissions. The model identifies the effects of 14 indicators on six travel modes: private cars, buses, metros, electric bikes, shared bikes, and walking, and simulates a level of integration (LOI) to represent the built environment attractiveness for different travel modes. Based on the LOI, travel modal shares are predicted and translated into carbon and air pollutant emissions using a bottom-up approach. The model is applied to three metro-centered precincts with distinct urban forms in Nanjing. The results reveal substantial differences in travel emissions across urban forms: in the car-dependent suburban precinct, the private car modal share reaches 38.7%, while in the transit-rich historic core it is 15.0%, leading to per capita annual carbon dioxide (CO₂) emissions of 857 kg and 429 kg, respectively. Most air pollutant emissions follow trends similar to CO₂, except for sulfur dioxide (SO₂), which exhibits higher per capita emissions in the historic core due to larger shares of electricity-based modes. A TOD retrofitting scenario for the suburban precinct indicates that targeted interventions can reduce travel CO₂ emissions by 6.1%, and air pollutant emissions by 2.8%–6.4%. Overall, the proposed model provides a tool to assess travel-related emissions across contrasting urban forms and identify TOD pathways for low-carbon travel and improved air quality.

The fine particulate matter (PM2.5) emitted during cooking is a significant contributor to household air pollution in rural China, resulting in millions of premature deaths annually. Since cooking is an internal pollution source, the indoor concentration of cooking-generated PM2.5 can vary among different rooms in multizone rural residences. This study provides a comprehensive understanding of indoor PM2.5 from cooking in rural residences by utilizing on-site investigations to gather information on cooking behavior and dwelling layout in three Chinese villages, and subsequently simulating indoor spatiotemporal concentrations of cooking-generated PM2.5 using a multizone model. Our findings indicate that the type of zone significantly influences the zonal concentration of PM2.5, with the highest concentrations found in kitchens (i.e., 13.9 to 188.0 μg/m³) and lowest in non-adjacent zones to the kitchen (i.e., 0.01 to 7.5 μg/m³) among all the modeled conditions. More importantly, the study also assesses the resulting personal exposures for occupants with different time-spent patterns, revealing that the main cook at home and preferring to stay in the adjacent rooms to the kitchen are at the highest risk for personal exposure. The highest personal exposure levels of cooking-generated PM2.5 are 28.5 ± 30.1 μg/m³, which is 34 times that of occupants who stay away from the kitchen. The study provides a deeper scientific insight into the indoor spatial distribution and personal exposure to cooking-generated PM2.5 in rural residences, which is crucial for developing effective interventions to mitigate the detrimental health impacts of household air pollution in rural areas.