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.
This paper seeks to evaluate and calibrate data collected by low-cost particulate matter (PM) sensors in different environments and using different aggregated temporal units (i.e., 5-s, 1-min, 10-min, 30 min intervals). We first collected PM concentrations (i.e., PM1, PM2.5, and PM10) data in five different environments (i.e., indoor and outdoor of an office building, a train platform and lobby of a subway station, and a seaside location) in Hong Kong, using five AirBeam2 sensors as the low-cost sensors and a TSI DustTrak DRX Aerosol Monitor 8533 as the reference sensor. By comparing the collected PM concentrations, we found high linearity and correlation between the data reported by the AirBeam2 sensors in different environments. Furthermore, the results suggest that the accuracy and bias of the PM data reported by the AirBeam2 sensors are affected by rainy weather and environments with high humidity and a high level of hygroscopic salts (i.e., a seaside location). In addition, increasing the aggregation level of the temporal units (i.e., from 5-s to 30 min intervals) increases the correlation between the PM concentrations obtained by the AirBeam2 sensors, while it does not significantly improve the accuracy and bias of the data. Lastly, our results indicate that using a machine learning model (i.e., random forest) for the calibration of PM concentrations collected on sunny days generates better results than those obtained with multiple linear models. These findings have important implications for researchers when designing environmental exposure studies based on low-cost PM sensors.
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