In recent years, the adverse health effects attributed to air pollution have been a focus of intense study. Exposure to pollutants such as airborne particulate matter (PM) and ozone (O3) has been associated with increases in morbidity and mortality due to respiratory and cardiovascular diseases. The aim of this study was to determine a correlation between particles (PM10, PM2.5) and O3 with hospital admissions in Setúbal, a densely populated Portuguese urban region that coexists with a heavy industrial area. A database with daily air quality and hospital admission data over 5 years (2005-2009) was assembled and associations were investigated by ordinary least squares linear regression. Results showed positive significant associations between PM10 and respiratory diseases for ages below 14 yr and above 64 yr, and between PM2.5 and respiratory diseases for ages above 64 yr.

Ambient air pollution is recognised as one of the potential environmental risk factors causing health hazards to the exposed population, demonstrated in numerous previous studies. Several longitudinal, ecological and epidemiological studies have shown associations between outdoor levels of outdoor atmospheric pollutants and adverse health effects, especially associated with respiratory and cardiovascular hospital admissions. The aim of this work is to assess the influence of atmospheric pollutants over the hospital admissions in Lisbon, by Ordinary Least Squares Linear Regression. The pollutants (CO, NO, NO2, SO2, O3, PM10 and PM2.5) were obtained from 13 monitoring stations of the Portuguese Environmental Agency, which provide hourly observations. Hospital admission data were collected from the Central Administration of the Health System and were compiled by age: <15, 15-64, >64 years old. The study period was 2006-2008. Results showed significant positive associations between the following: (1) the pollutants CO, NO, NO2, SO2, PM10 and PM2.5 and circulatory diseases for ages between 15 and 64 years (0.5% hospital admissions (HA) increase with 10 μg m(-3) NO increase) and above 64 years (1.0% stroke admission increase with 10 μg m(-3) NO2 increase); (2) the pollutants CO, NO, NO2, SO2, PM10 and PM2.5 and respiratory diseases for ages below 15 years (up to 1.9% HA increase with 10 μg m(-3) pollutant increase); and (3) the pollutants NO, NO2 and SO2 and respiratory diseases for ages above 64 years (1.3% HA increase with 10 μg m(-3) CO increase).

a Studies of the acute health effects of air pollution have used exposure windows of different spans and related them to single-day responses. Little is known about whether an increased response window span might be a viable alternative to single-day responses. Our aim is to compare a new model specification where both the exposure and response variables are represented as 7 day moving averages (CMA&CMA model) with the most widely used model specifications in the literature, where the response variable is usually a single-day, in terms of coefficients and their precision and robustness. To this end, daily series of 12 emergency-related hospital admissions and 6 air pollutants spanning 5.5 years in Lisbon were analysed through single-pollutant linear regression and, when necessary M-estimation. With our data, the CMA&CMA model yields coefficients that are very close to models where only the exposure variable is specified as a moving average whether the latter are estimated by OLS or robust M-estimation. In addition, the CMA&CMA model leads to more precise and robust estimates than other model specifications. The new model specification is a straightforward tool for adjusting weekend effects and errors. It is also analogous to robust estimation, with the added advantages of being sensitive to extreme values that are clustered in time, and leading to more precise and robust estimates without loss of high-frequency information. One drawback is the induction of autocorrelation in the residuals.

The present paper focuses on biomonitoring of elemental atmospheric pollution, which is reviewed in terms of larger-scaled biomonitoring surveys in an epidemiological context. Based on the literature information, today’s availability of solar-powered small air filter samplers and fibrous ion exchange materials is regarded as adequate or an even better alternative for biomonitor transplant materials used in small-scaled set-ups, but biomonitors remain valuable in larger-scaled set-ups and in unforeseen releases and accidental situations. In the latter case, in-situ biomonitoring is seen as the only option for a retrospective study: biomoniors are there before one even knows that they are needed. For biomonitoring, nuclear analytical techniques are discussed as key techniques, especially because of the necessary multi-element assessments in both source recognition and single-element interpretation. To live up to the demands in an epidemiological context, larger-scaled in-situ biomonitoring asks for large numbers of samples, and consequently, for large total sample masses, this all to ensure representation of both local situations and survey area characteristics. Possibly, this point should direct studies into new “easy-to-sample” biomonitor organisms, of which high masses and numbers may be obtained in field work, rather than continue with biomonitors such as lichens. This also means that both sample handling and processing are of key importance in these studies. To avoid problems in comparability of analytical general procedures in milling, homogenization and digestion of samples of large masses, the paper proposes to involve only few but high-quality laboratories in the total element assessment routines. In this respect, facilities that can handle large sample masses in the assessment of element concentrations are to be preferred. This all highlights the involvement of large-sample-volume nuclear facilities, which, however, should be upgraded and automated in their operation to ensure the necessary sample throughput in larger-scaled biomonitoring.