(1) The association of the indoor environmental conditions in classrooms with illness-related absenteeism (IRA) was not well investigated. In addition, studying the association between heating and non-heating seasons were very limited; (2) To fill this knowledge gap, a research team collected various indoor air quality (IAQ) and thermal comfort conditions (TC) of 85 elementary classrooms in two school districts from the Midwestern United States throughout an academic year; in total, 255 classroom visits were performed. A negative binomial regression model was implied to associate the classroom’s IAQ and TC with IRA, separating for heating and non-heating seasons; (3) During non-heating season, a 3% increase of IRA was estimated with 1,000,000-counts/L increase of particles that had a diameter less than 2.5 μm (PN2.5); during the heating season, a 3% increase of IRA were expected with 100 ppm increase of room averaged CO₂ concentration; and (4) These results suggested that the IAQ and TC factors could associated with IRA differently between heating and non-heating seasons.

The carbon emissions released from buildings are correlated with various factors in social and economic systems. Thus, quantifying and then controlling those factors can decrease the release of carbon emissions further. To quantify the influencing factors of the carbon emissions of residential buildings in Yunnan Province in China, separately for urban and rural areas, this study adopted the methods of utilizing the carbon emission factor and the LMDI model and combined them with the carbon emissions data obtained from 2010 to 2019. Subsequently, with this model, the contribution of each factor to the overall carbon emissions was quantified. The results demonstrate the following: (1) the main factors influencing carbon emissions from residential buildings include the per capita floor area, energy consumption per unit area, energy intensity effect, energy structure effect, urbanization rate, and population size. (2) For urban buildings, carbon emissions are negatively correlated with the energy consumption per unit area, energy intensity effect, and energy structure effect, with contribution values of 0.34, 0.27, and 0.05, respectively. Conversely, there is a positive correlation with the per capita floor area, urbanization rate, and population size, with contribution values of 0.23, 0.11, and 0.01, respectively. (3) For rural buildings, carbon emissions are negatively correlated with urbanization rate, energy intensity effect, and energy structure effect, with contribution values of 0.16, 0.15, and 0.14, respectively. Conversely, there is a positive correlation with the per capita floor area, energy consumption per unit area, and population size, with contribution values of 0.29, 0.24, and 0.02, respectively.

To investigate concentration and potential sources of total gaseous mercury (TGM) in a concentrated non-ferrous metals smelting area in southwest China, a high temporal resolution automatic mercury meter was used to measure TGM in the environment and the emissions from major sources of Mengzi city. The average concentration of TGM in urban air was 2.1 ± 3.5 ng·m⁻³ with a range of 0.1~61.1 ng·m⁻³ over the study period. The highest TGM concentration was in fall (3.3 ± 4.3 ng·m⁻³). The daytime TGM concentration (2.8 ± 3.5 ng·m⁻³) was significantly higher than that in the nighttime (1.6 ± 1.1 ng·m⁻³), which may be attributed to the increased emissions of mercury from the high volume of vehicle activity during the day. To discuss the contributions of local sources and long-range transport, eight pollution events were identified based on the ratio of ΔTGM/ΔCO (Carbon Monoxide), which can be found that local sources are a key contributor to the major TGM pollution events. Concentrations of TGM in flue gases from eight non-ferrous industrial sources were also measured in Mengzi, which were found that the highest TGM emission concentration was up to 4.6 mg·m⁻³. Simultaneously, the concentrations of TGM in ambient air around these industries and Xidu Tunnel were also detected, the concentrations were 1 to 4 times higher than that in the urban air sampling site. Based on the analysis of air mass and PSCF, when northwest wind happened, these emissions of industries and vehicles can be identified as the primary sources of TGM in urban air of Mengzi.

The advancement of urbanization can exacerbate the heat island effect, bringing certain impacts and challenges to human settlement environment and sustainable development. Land Surface Temperature is a crucial metric for highlighting variations in urban thermal environments. Utilizing Landsat-8 OLI/TIRS satellite remote sensing data, this research offers a detailed examination of the spatial distribution and changes in LST through the lens of Local Climate Zones. The results indicated that: (1) The area occupied by built-up types is 13.68% lower than that of natural cover types. Additionally, urbanization has led to the fragmentation of blue-green ecosystems in central urban regions. (2) The high temperature zone (24.11-27.75 °C) occupies 3.43% of the total area, mainly found in developed regions along the study area's boundary and in patches of bare soil within urban districts. In contrast, the low-temperature zone (11.64-13.60 °C) covers 13.11%, primarily including Dianchi Lake and several smaller scattered lakes. The distribution of temperature zones is closely linked to building functions and the arrangement of blue-green ecosystems. (3) Overall, the LST of developed land types is higher compared to natural cover types, while high-rise buildings with low density can achieve more effective cooling. Among natural categories, the average LSTs of dense trees and water is 5.3 °C lower than built-up areas, showing a strong cooling effect. In contrast, paved ground and bare soil have average LSTs about 2 °C higher than builtup areas. This research offers a theoretical foundation for optimizing building layout and blue-green ecosystem design, contributing to the enhancement of the thermal environment.

To investigate the thermal and relative humidity characteristics during the dry season in Kunming's Chenggong New District, this study selected three observation routes and conducted parallel mobile observations on November 27 th-28th, 2022, and January 10 th-11th, 2023. The routes encompassed various land use types and underlying surfaces. After processing the data, the results were imported into ArcGIS 10.6 to create band-like distribution maps of temperature and relative humidity for subsequent analysis. The results demonstrate that in the study area, daytime urban heat island intensity slightly surpasses nighttime intensity during the dry season, with an average daytime urban heat island intensity of 2.5 °C and a nighttime urban heat island intensity of 2.1 °C. A significant urban heat island effect is observed in industrial and residential areas. The rates of change for temperature and humidity are 1.3,-0.6 °C per h, and-3.9, 2.7% per h, respectively, with daytime variations being significantly greater than nighttime ones.