The effects of artificial light at night (ALAN) on human health have drawn increased attention in the last two decades. Numerous studies have discussed the effects of ALAN on human health on diverse topics. A broader scope of how ALAN may affect human health is thus urgently needed. This paper depicts a systematic evidence map in a multi-component framework to link ALAN with human health through a comprehensive literature review of English research articles in the past two decades. A three-phase systematic review was conducted after a generalized search of relevant articles from three publication databases, namely Scopus, the Web of Science, and PubMed. In total, 552 research articles were found in four categories and on numerous topics within our framework. We cataloged the evidence that shows direct and indirect as well as positive and negative effects of ALAN on human physical and mental health. We also summarized the studies that consider ALAN as a social determinant of human health. Based on our framework and the systematic evidence map, we also suggest several promising directions for future studies, including method design, co-exposure and exposome studies, and social and environmental justice.

Dense unconventional shale gas extraction activities have occurred in Appalachian Ohio since 2010 and they have caused various landcover changes and forest fragmentation issues. This research investigated the most recent boom of unconventional shale gas extraction activities and their impacts on the landcover changes and forest structural changes in the Muskingum River Watershed in Appalachian Ohio. Triple-temporal high-resolution natural-color aerial images from 2006 to 2017 and a group of ancillary geographic information system (GIS) data were first used to digitize the landcover changes due to the recent boom of these unconventional shale gas extraction activities. Geographic object-based image analysis (GEOBIA) was then employed to form forest patches as image objects and to accurately quantify the forest connectivity. Lastly, the initial and updated forest image objects were used to quantify the loss of core forest as the two-dimensional (2D) forest structural changes, and initial and updated canopy height models (CHMs) derived from airborne light detection and ranging (LiDAR) point clouds were used to quantify the loss of forest volume as three-dimensional (3D) forest structural changes. The results indicate a consistent format but uneven spatiotemporal development of these unconventional shale gas extraction activities. Dense unconventional shale gas extraction activities formed two apparent hotspots. Two-thirds of the well pad facilities and half of the pipeline right-of-way (ROW) corridors were constructed during the raising phase of the boom. At the end of the boom, significant forest fragmentation already occurred in both hotspots of these active unconventional shale gas extraction activities, and the areal loss of core forest reached up to 14.60% in the densest concentrated regions of these activities. These results call for attention to the ecological studies targeted on the forest fragmentation in the Muskingum River Watershed and the broader Appalachian Ohio regions.

As public awareness of air quality issues becomes heightened, people’s perception of air quality is drawing increasing academic interest. However, data about people’s perceived environment need scrutiny before being used in environmental health studies. In this research, we examine the associations between people’s perceptions of air quality and their self-reported respiratory health symptoms. Spearman rank correlation coefficients were estimated and the associations were tested at the 95% confidence level. Using data collected from participants in two representative communities in Hong Kong, the results indicate a weak but significant association between people’s perceived air quality and their self-reported frequency of respiratory symptoms. However, there are disparities in such an association between different genders, age groups, household income levels, education levels, marital statuses, and geographic contexts. The most striking disparities are between genders and geographic contexts. Multiple significant associations were observed for male participants (correlation coefficients: 0.169~0.205, p-values: 0.021~0.049), while none was observed for female participants. Besides, multiple significant associations were observed in the old town (correlation coefficients: 0.164~0.270, p-values: 0.003~0.048), while none was observed in the new town. The results have significant implications for environmental health research using social media data, whose reliability depends on the association between people’s perceived or actual environments and their health outcomes. Since inconsistent associations exist between different groups of people, researchers need to scrutinize social media data before using them in health studies.

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