Atmosphere

Perceived dry air is a common complaint in indoor environments, yet its health associations and environmental factors related to this perception are unclear. We surveyed 7865 families and measured the indoor environment in 399 dwellings in Tianjin, China, from 2013 to 2016. It was found that 10% of the surveyed families reported frequently perceived dry air. The dry air perception was significantly associated with wheeze (adjusted odds ratio (AOR) = 2.60), rhinitis (AOR = 1.91), eczema (AOR = 1.89), and common cold infections (AOR = 1.64) in children and sick building syndrome symptoms in adults (AOR: 2.63–8.59). Higher concentrations of di-isobutyl (DiBP) and benzyl butyl phthalate (BBzP) were observed in homes with dry air perception. Although higher relative humidity might reduce the perception of dry air (AOR = 0.66), lower air exchange rates attenuated the protective effect. Additionally, building characteristics related to pollution exposures, such as living near highways (AOR = 1.31), visible mold spots (AOR = 1.50), and suspected moisture problems (AOR = 1.88), were associated with indoor dry air perception. Our findings suggest that perceived dry air was correlated with indoor exposure to pollution and could be used as an indicator for sick buildings.

The spatial quantification of multiple sources within the urban environment is crucial for understanding urban air quality and implementing measures to mitigate air pollution levels. At the same time, emissions from road traffic contribute significantly to these concentrations. However, uncertainties arise when assessing the contribution of multiple sources affecting a single receptor. This study aims to evaluate an inverse dispersion modelling methodology that combines Computational Fluid Dynamics (CFD) simulations with the Metropolis–Hastings Markov Chain Monte Carlo (MCMC) algorithm to quantify multiple traffic emissions at the street scale. This approach relies solely on observational data and prior information on each source’s emission rate range and is tested within the Augsburg city centre. To address the absence of extensive measurement data of a real pollutant correlated with traffic emissions, a synthetic observational dataset of a theoretical pollutant, treated as a passive scalar, was generated from the forward dispersion model, with added Gaussian noise. Furthermore, a sensitivity analysis also explores the influence of sensor configuration and prior information on the accuracy of the emission estimates. The results indicate that, when the potential emission rate range is narrow, high-quality predictions can be achieved (ratio between true and estimated release rates, $\mathsf{\Delta }q\le 2$) even with networks using data from only 10 sensors. In contrast, expanding the allowable emission range leads to reduced accuracy ( ), particularly in networks with fewer than 50 sensors. Further research is recommended to assess the methodology’s performance using real-world measurements.

Heavy-duty diesel trucks (HDDTs) are recognized as significant sources of air pollutants and greenhouse gases (GHGs) along freight corridors in port cities. Despite their impact, few studies have provided detailed spatiotemporal insights into their emissions within port-adjacent highway systems. This study presents a high-resolution, hourly emission inventory at the road-segment level for six major expressways in Shanghai, one of China’s leading port cities. The emission estimates are derived using a locally adapted COPERT V model, calibrated with HDDT GPS trajectory data and detailed road network information from OpenStreetMap. The inventory quantifies emissions of CO₂, NO_x, CO, PM, and VOCs, highlighting distinct temporal and spatial variation patterns. Weekday emissions consistently exceed those of weekends, with three prominent traffic-related peaks occurring between 5:00–7:00, 10:00–12:00, and 14:00–16:00. Spatial analysis identifies the G1503 and S20 expressways as major emission corridors, with S20 exhibiting particularly high emission intensity relative to its length. Combined spatiotemporal patterns reveal that weekday emission hotspots are more concentrated, reflecting typical freight activity cycles such as morning dispatch and afternoon return. The findings provide a scientific basis for formulating more precise emission control measures targeting HDDT operations in urban port environments.