Research examining associations between weather and human health frequently includes the effects of atmospheric humidity. A large number of humidity variables have been developed for numerous purposes, but little guidance is available to health researchers regarding appropriate variable selection. We examine a suite of commonly used humidity variables and summarize both the medical and biometeorological literature on associations between humidity and human health. As an example of the importance of humidity variable selection, we correlate numerous hourly humidity variables to daily respiratory syncytial virus isolates in Singapore from 1992 to 1994. Most water-vapor mass based variables (specific humidity, absolute humidity, mixing ratio, dewpoint temperature, vapor pressure) exhibit comparable correlations. Variables that include a thermal component (relative humidity, dewpoint depression, saturation vapor pressure) exhibit strong diurnality and seasonality. Humidity variable selection must be dictated by the underlying research question. Despite being the most commonly used humidity variable, relative humidity should be used sparingly and avoided in cases when the proximity to saturation is not medically relevant. Care must be taken in averaging certain humidity variables daily or seasonally to avoid statistical biasing associated with variables that are inherently diurnal through their relationship to temperature.

The health impacts of heat waves are an emerging environmental health concern. This is especially so for large cities where there is a concentration of people and because of the urban heat island effect. Temperatures within cities can reach stressful levels during extreme temperature events. To better manage heat related health risks, information is required on the intra-urban variability of vulnerability to heat wave events. Accordingly a heat vulnerability index (HVI) is developed and presented for Greater London in the United Kingdom. The approach to HVI development adopted is an inductive one whereby nine proxy measures of heat risk are extracted from the 2001 London census for 4765 census districts and subject to principal components analysis. Scores for the emergent principal components are weighted according to the variance they explain and summed to form the HVI. Although mapping of the HVI shows what appears to be a heterogeneous heat “risk-scape” statistical testing reveals significant spatial clustering of areas of high heat vulnerability in central and east London which also co-occur with areas of potentially high heat exposure. Drivers of the spatial pattern of heat vulnerability are discussed as are the implications of study results for heat risk management in large cities

Objective: Because the state of the atmosphere determines the development, transport, dispersion, and deposition of air pollutants, there is concern that climate change could affect morbidity and mortality associated with elevated concentrations of these gases and fine particles. We review how climate change could affect future concentrations of tropospheric ozone and particulate matter (PM), and what changing concentrations could mean for population health. Data sources: We review studies projecting the impacts of climate change on air quality and studies projecting the impacts of these changes on morbidity and mortality. Data synthesis: Climate change could affect local to regional air quality through changes in chemical reaction rates, boundary layer heights that affect vertical mixing of pollutants, and changes in synoptic airflow patterns that govern pollutant transport. Sources of uncertainty include the degree of future climate change, future emissions of air pollutants and their precursors, and how population vulnerability may change in the future. Given these uncertainties, projections suggest that climate change will increase concentrations of tropospheric ozone, at least in high-income countries when precursor emissions are held constant, which would increase morbidity and mortality. Few projections are available for low- and middle-income countries. The evidence is less robust for PM, primarily because few studies have been conducted. Conclusions: Additional research is needed to better understand the possible impacts of climate change on air pollution–related health impacts. If improved models continue to project higher ozone concentrations with climate change, then reducing greenhouse gas emissions would enhance the health of current and future generations.

Objectives We previously developed a model for projection of heat-related mortality attributable to climate change. The objective of this paper is to improve the fit and precision of and examine the robustness of the model. Methods We obtained daily data for number of deaths and maximum temperature from respective governmental organizations of Japan, Korea, Taiwan, the USA, and European countries. For future projection, we used the Bergen climate model 2 (BCM2) general circulation model, the Special Report on Emissions Scenarios (SRES) A1B socioeconomic scenario, and the mortality projection for the 65+-year-old age group developed by the World Health Organization (WHO). The heat-related excess mortality was defined as follows: The temperature–mortality relation forms a V-shaped curve, and the temperature at which mortality becomes lowest is called the optimum temperature (OT). The difference in mortality between the OT and a temperature beyond the OT is the excess mortality. To develop the model for projection, we used Japanese 47-prefecture data from 1972 to 2008. Using a distributed lag nonlinear model (two-dimensional nonparametric regression of temperature and its lag effect), we included the lag effect of temperature up to 15 days, and created a risk function curve on which the projection is based. As an example, we perform a future projection using the above-mentioned risk function. In the projection, we used 1961–1990 temperature as the baseline, and temperatures in the 2030s and 2050s were projected using the BCM2 global circulation model, SRES A1B scenario, and WHO-provided annual mortality. Here, we used the “counterfactual method” to evaluate the climate change impact; For example, baseline temperature and 2030 mortality were used to determine the baseline excess, and compared with the 2030 excess, for which we used 2030 temperature and 2030 mortality. In terms of adaptation to warmer climate, we assumed 0 % adaptation when the OT as of the current climate is used and 100 % adaptation when the OT as of the future climate is used. The midpoint of the OTs of the two types of adaptation was set to be the OT for 50 % adaptation. Results We calculated heat-related excess mortality for 2030 and 2050. Conclusions Our new model is considered to be better fit, and more precise and robust compared with the previous model.