Fine simulation of PM2.5 combined with NPP-VIIRS night light remote sensing and mobile monitoring data

Human activity plays a crucial role in influencing PM2.5 concentration and can be assessed through nighttime light remote sensing. Therefore, it is important to investigate whether the nighttime light brightness can enhance the accuracy of PM2.5 simulation in different stages. Utilizing PM2.5 mobile monitoring data, this study introduces nighttime lighting brightness as an additional factor in the PM2.5 simulation model across various time periods. It compares the differences in simulation accuracy, explores the impact of nocturnal human activities on PM2.5 concentrations at different periods of the following day, and analyzes the spatial and temporal pollution pattern of PM2.5 in urban functional areas. The results show that (1) the incorporation of nighttime lighting brightness effectively enhances the model's accuracy (R2), with an improvement ranging from 0.04 to 0.12 for different periods ranges. (2) the model's accuracy improves more prominently during 8:00-12:00 on the following day, and less so during 12:00-18:00, as the PM2.5 from human activities during the night experiences a strong aggregation effect in the morning of the next day, with the effect on PM2.5 concentration declining after diffusion until the afternoon. (3) PM2.5 is primarily concentrated in urban functional areas including construction sites, roads, and industrial areas during each period. But in the period of 8:00-12:00, there is a significant level of PM2.5 pollution observed in commercial and residential areas, due to the human activities that occurred the previous night.

Modeling spatiotemporal carbon emissions for two mega-urban regions in China using urban form and panel data analysis

Spatiotemporal monitoring of urban CO₂ emissions is crucial for developing strategies and actions to mitigate climate change. However, most spatiotemporal inventories do not adopt urban form data and have a coarse resolution of over 1 km, which limits their implications in intra-city planning. This study aims to model the spatiotemporal carbon emissions of the two largest mega-urban regions in China, the Yangtze River Delta and the Pearl River Delta, using urban form data from the Local Climate Zone scheme and landscape metrics, nighttime light images, and a year-fixed effects model at a fine resolution from 2012 to 2016. The panel data model has an R² value of 0.98. This study identifies an overall fall in carbon emissions in both regions since 2012 and a slight elevation of emissions from 2015 to 2016. In addition, urban compaction and integrated natural landscapes are found to be related to low emissions, whereas scattered low-rise buildings are associated with rising carbon emissions. Furthermore, this study more accurately extracts urban areas and can more clearly identify intra-urban variations in carbon emissions than other datasets. The open data supported methodology, regression models, and results can provide accurate and quantifiable evidence at the community level for achieving a carbon-neutral built environment.

Impact analysis of COVID-19 pandemic control measures on nighttime light and air quality in cities

The COVID-19 pandemic has profoundly affected human society on a global scale. COVID-19 pandemic control measures have led to significant changes in nighttime light (NTL) and air quality. Four cities that were severely impacted by the pandemic and that implemented different pandemic control measures, namely, Wuhan (China), Delhi (India), New York (United States), and Rome (Italy), were selected as study areas. The Visible Infrared Imaging Radiometer Suite (VIIRS) and air quality data were used to study the variation characteristics of NTL and air quality in the four cities in 2020. NTL brightness in Wuhan, Delhi, New York, and Rome decreased by 8.88%, 17.18%, 8.21%, and 6.33%, respectively, compared with pre-pandemic levels; in the resumption phase Wuhan and Rome NTL brightness recovered by 13.74% and 3.38%, but Delhi and New York decreased by 16.23% and 4.99%. Nitrogen dioxide (NO₂) concentrations in the lockdown periods of Wuhan, Delhi, New York, and Rome decreased by 65.07%, 68.75%, 55.59%, and 56.81%, respectively; PM_2.5 decreased by 49.25%, 69.40%, 52.54%, and 66.67%. Air quality improved, but ozone (O₃) concentrations increased significantly during the lockdown periods. The methods presented herein can be used to investigate the impact of pandemic control measures on urban lights and air quality.

Association of exposure to artificial light at night with atopic diseases: A cross-sectional study in college students

The impact of artificial light at night (ALAN) exposure on health has become increasingly prominent. However, little is known about the effect of ALAN exposure on atopic diseases. In this study, a cross-sectional analysis of incoming students was conducted in 5 geographically disperse universities which locate in Changsha (south), Wuhan (central), Xiamen (east), Urumchi (west), and Hohhot (north), respectively. All incoming students who consented to participate were recruited, followed by a health examination and a questionnaire survey. Prevalent atopic diseases were diagnosed by clinicians. Mean ALAN (nanoWatts/cm²/sr) during their adolescence was obtained from the remote sensing observed nighttime light data matching with their residence information, which was obtained from survey. Mixed generalized linear models (log-binomial) were used to estimate the associations, in terms of prevalence ratio (PR) with 95% confidence interval (CI). A total of 20106 participants were included in the analysis. Based on previous work, we chose factors including socioeconomic status, behavioural factors, major air pollutants, and air climatic parameters for adjustment. After full adjustment, the PR for atopic diseases was 1.35 (95% CI: 1.27-1.42; P < 0.001). The effect size of ALAN was the largest for asthma (PR = 1.80; 95% CI: 1.48-2.19; P < 0.001), followed by atopic rhinitis (PR = 1.42; 95% CI: 1.33-1.51; P < 0.001), and atopic dermatitis (PR = 1.20; 95% CI: 1.06-1.35; P = 0.003). Subgroup analyses by covariates showed consistent results. This study revealed that exposure to ALAN during adolescence may contribute to a higher risk of atopic diseases in young adulthood.

Detection of County Economic Development Using LJ1-01 Nighttime Light Imagery: A Comparison with NPP-VIIRS Data

Nighttime light (NTL) remote sensing data have been widely used to derive socioeconomic indicators at the national and regional scales to study regional economic development. However, most previous studies only chose a single measurement indicator (such as GDP) and adopted simple regression methods to investigate the economic development of a certain area based on DMSP-OLS or NPP-VIIRS stable NTL data. The status quo shows the problems of using a single evaluation index—it has a low evaluation precision. The LJ1-01 satellite is the first dedicated NTL remote sensing satellite in the world, launched in July 2018. The data provided by LJ1-01 have a higher spatial resolution and fewer blooming phenomena. In this paper, we compared the accuracy of the LJ1-01 data and NPP-VIIRS data in detecting county-level multidimensional economic development. In three provinces in China, namely, Hubei, Hunan and Jiangxi, 20 socioeconomic parameters were selected from the following five perspectives: economic conditions, people’s livelihood, social development, public resources and natural vulnerability. Then, a County-level Economic Index (CEI) was constructed to evaluate the level of multidimensional economic development, with the spatial pattern of the multidimensional economic development also identified across the study area. The present study adopted the random forest (RF) and linear regression (LR) algorithms to establish the regression model individually, and the results were evaluated by cross-validation. The results show that the RF algorithm greatly improves the accuracy of the model compared with the LR algorithm, and thus is suitable for the study of NTL data. In addition, a better determinate coefficient (R²) based on the LJ1-01 data (0.8168) was obtained than that from the NPP-VIIRS data (0.7245) in the RF model, which reflects that the LJ1-01 data offer better potential in the evaluation of socioeconomic parameters and can be used to identify, both accurately and efficiently, multidimensional economic development at the county level.