Linking nighttime outdoor lighting attributes to pedestrians' feeling of safety: An interactive survey approach.

Public space lighting (PSL) contributes to pedestrians' feeling of safety (FoS) in urban areas after natural dark. However, little is known how different PSL attributes, such as illuminance, light temperature, uniformity and glare, affect people's FoS in different contextual settings. The present study aims to bridge this knowledge gap by developing a model linking different PSL attributes with FoS, while controlling for individual, locational, environmental and temporal factors. To develop such model, the study employs a novel interactive user-oriented method, based on a specially-designed mobile phone application-CityLightsTM. Using this app, a representative sample of observers reported their impressions of PSL attributes and FoS in three cities in Israel, following a set of predetermined routes and points. As the study shows, higher levels of illumination and uniformity positively affect FoS, while lights perceived as warm tend to generate higher FoS than lights perceived as cold. These findings may guide future illumination polices aimed at promoting energy efficiency while ensuring urban sustainability.

Spatial identification of environmental health hazards potentially associated with adverse birth outcomes.

Reduced birth weight (RBW) and reduced head circumference (RHC) are adverse birth outcomes (ABOs), often linked to environmental exposures. However, spatial identification of specific health hazards, associated with these ABOs, is not always straightforward due to presence of multiple health hazards and sources of air pollution in urban areas. In this study, we test a novel empirical approach to the spatial identification of environmental health hazards potentially associated with the observed RHC and RBW patterns. The proposed approach is implemented as a systematic search, according to which alternative candidate locations are ranked based on the strength of association with the observed birth outcome patterns. For empirical validation, we apply this approach to the Haifa Bay Area (HBA) in Israel, which is characterized by multiple health hazards and numerous sources of air pollution. We identified a spot in the local industrial zone as the main risk source associated with the observed RHC and RBW patterns. Multivariate regressions, controlling for personal, neighborhood, and geographic factors, revealed that the relative risks of RHC and RBW tend to decline, other things being equal, as a function of distance from the identified industrial spot. We recommend the proposed identification approach as a preliminary risk assessment tool for environmental health studies, in which detailed information on specific sources of air pollution and air pollution dispersion patterns is unavailable due to limited reporting or insufficient monitoring.

Environmental risk factors associated with low birth weight: The case study of the Haifa Bay Area in Israel.

BACKGROUND: Low birth weight (LBW) is known to be associated with infant mortality and postnatal health complications. Previous studies revealed strong relationships between LBW rate and several socio-demographic factors, including ethnicity, maternal age, and family income. However, studies of association between LBW rate and environmental risk factors remain infrequent. STUDY METHODS: We retrieved a geo-referenced data set, containing 7216 individual records of children born in 2015 in the Haifa Bay Area in Israel. Using this dataset, we analysed factors affecting LBW prevalence by applying two alternative techniques: analysis of LBW rates in small census area (SCAs) and more recently developed double kernel density (DKD) relative risk (RR) estimates. RESULTS: In the SCA models, LBW rate was found to be associated with proximity to petrochemical industries (B=-0.26, 95%CI=-0.30, -0.22), road density (B=0.05, 95%CI=0.02, 0.08), distance to the seashore (B=0.17, 95%CI=0.14, 0.22), PM2.5 (B=0.06, 95%CI=0.04, 0.09) and NOx (B=0.10, 95%CI=0.06, 0.13) exposure estimates. Although similar factors emerged in the DKD models as well, in most cases, the effects of these factors in the latter models were found to be stronger: proximity to petrochemical industries (B=-0.48, 95%CI= -0.51, -0.30), road density (B=0.05, 95%CI=0.02, 0.08), distance to the seashore (B=0.24, 95%CI=0.21, 0.27), PM2.5 (B=0.08, 95%CI=0.05, 0.10) and NOx (B=0.20, 95%CI=0.17, 0.23) exposure estimates. In addition, elevation above the sea level was found to be statistically significant in spatial dependence models estimated for both DKD and SCA rates (P < 0.01). CONCLUSION: The analysis revealed an excess LBW rate in residential areas located close to petrochemical industries and a protective effect of seashore proximity and elevation above the sea level on the LBW rate. We attribute the latter finding to the moderating effect of elevated seashore locations on outdoor temperatures during the hot summer season.

A new approach to spatial identification of potential health hazards associated with childhood asthma.

RESEARCH BACKGROUND: Childhood asthma is a chronic disease, known to be linked to prolonged exposure to air pollution. However, the identification of specific health hazards, associated with childhood asthma is not always straightforward, due to the presence of multiple sources of air pollution in urban areas. In this study, we test a novel approach to the spatial identification of environmental hazards that have the highest probability of association with the observed asthma morbidity patterns. METHODS: The effect of a particular health hazard on population morbidity is expected to weaken with distance. To account for this effect, we rank potential health hazards based on the strength of association between the observed morbidity patterns and wind-direction weighted proximities to these locations. We validate this approach by applying it to a study of spatial patterns of childhood asthma in the Greater Haifa Metropolitan Area (GHMA) in Israel, characterised by multiple health hazards. RESULTS: We identified a spot in the local industrial zone as the primary risk source for the observed asthma morbidity patterns. Multivariate regressions, controlling for socio-economic and geographic variables, revealed that the observed incidence rates of asthma tend to decline as a function of distance from the identified industrial location. CONCLUSION: The proposed identification approach uses disease patterns as its main input, and can be used by researches as a preliminary risk assessment tool, in cases in which specific sources of locally elevated morbidity are unclear or cannot be identified by traditional methods.

Spatial identification of potential health hazards: a systematic areal search approach.

BACKGROUND AND AIMS: Large metropolitan areas often exhibit multiple morbidity hotspots. However, the identification of specific health hazards, associated with the observed morbidity patterns, is not always straightforward. In this study, we suggest an empirical approach to the identification of specific health hazards, which have the highest probability of association with the observed morbidity patterns. METHODS: The morbidity effect of a particular health hazard is expected to weaken with distance. To account for this effect, we estimate distance decay gradients for alternative locations and then rank these locations based on the strength of association between the observed morbidity and wind-direction weighted proximities to these locations. To validate this approach, we use both theoretical examples and a case study of the Greater Haifa Metropolitan Area (GHMA) in Israel, which is characterized by multiple health hazards. RESULTS: In our theoretical examples, the proposed approach helped to identify correctly the predefined locations of health hazards, while in the real-world case study, the main health hazard was identified as a spot in the industrial zone, which hosts several petrochemical facilities. CONCLUSION: The proposed approach does not require extensive input information and can be used as a preliminary risk assessment tool in a wide range of environmental settings, helping to identify potential environmental risk factors behind the observed population morbidity patterns.