Air pollution exposure monitoring and estimation

Abstract

In the winter of 1994, 2300 school-age children in Oslo participated in a panel study of the role of traffic pollution on the exacerbation of diseases of the respiratory system and other symptoms of reduced health and well being in children. The children filled out a diary daily with information for five time points over six weeks. In order to quantify exposure-effect relationships for the symptoms, individual exposure to NO2 and particulate matter (PM2.5) was estimated, using the DINEX method a combination of information from the diary as to the children's whereabouts during the five time points each day, coupled with continuous dispersion modelling. An individual exposure estimate for each time point for each child was defined. Individual exposure estimated using dispersion modelling can be used to examine patterns of exposure such as isolating geographic areas with higher concentrations or describing concentrations of pollution by time of day. The diary allowed the time-use of the children to be described.

A review and evaluation of intraurban air pollution exposure models

“…In fulfilling model assumptions, dispersion models require pollution, meteorological, and emission data. Data on pollution concentrations, also referred to as background concentrations, are usually obtained from government monitoring stations near the study area and are used for model calibration (Clench-Aas et al, 1999b). Meteorological data provide information about the wind speed, wind direction, ambient temperature, solar radiation and atmospheric stability class (Gualtieri and Tartaglia, 1998).…”

“…In an extensive study, also focused on traffic-related pollution, Clench-Aas et al (1999b) developed an integrated exposure monitoring system. It expanded on an existing air quality monitoring system using dispersion modelling.…”

“…The overall implementation cost is measured in three parts: (a) equipment cost, which includes the cost of monitoring devices (e.g., personal monitors, monitoring tubes), as well as computer hardware, which can be taxing for some of these models; (b) many of these models require sophisticated van Vliet et al (1997), Ciccone et al (1998), Wilkinson et al (1999), Venn et al (2000Venn et al ( , 2001, Wyler et al (2000), Janssen et al (2001), Hoek et al (2002), English et al (1999), Jerrett et al (2002), Mahesswaran and Elliot (2003), Langholz et al (2002) Yes Interpolation Mulholland et al (1998) Jerrett et al (2001a, Finkelstein et al (2003), Pikhart et al (2001), Ritz et al (2000), Abbey et al (1999) Yes Land use regression Briggs et al (1997), Lebret et al (2000), Briggs (2000), Brauer et al (2002), Brauer et al (2003), Yes Dispersion Hruba et al (2001), Clench-Aas et al (1999b, Walker et al, 1999), Bartonova et al (1999), Anderson et al (1996), Potoglou and Kanaroglou (2002), Benson (1989), Bellander et al (2001), SMHI (1993), Gualtieri and Tartaglia (1998), Nyberg et al (2000), Nafstad et al (2003) Yes Integrated Meteorological Emissions Nicholls et al (1993),…”

Effects of moisture damage and renovation on microbial conditions and pupils’ health in two schools—a longitudinal analysis of five years

“…Together with home, school is the most important indoor environment for children aged between six and sixteen: primary school children spend 20% of the time in school. 15 Public awareness of adverse health effects of moisture and mold damage has created a pressure to repair observed damage. Attending a moisture-damaged school increases the risk of respiratory symptoms among children, but after renovation, the overall health status of the pupils improves.…”

Children are reliable reporters of common symptoms: results from a self‐reported symptom diary for primary school children

“…The children’s diary (Fig. 1) was especially designed to enable also children without a full command of written language to fill in the required data (19,20). The symptoms queried are listed in Table 1.…”