924 resultados para 770701 Air quality
Resumo:
Indoor Air Quality (IAQ) can have significant implications for health, productivity, job performance, and operating cost. Professional experience in the field of indoor air quality suggests that high expectations (better than nationally established standards) (American Society of Heating, Refrigerating, and Air-conditioning Engineers (ASHRAE)) of workplace indoor air quality lead to increase air quality complaints. To determine whether there is a positive association between expectations and indoor air quality complaints, a one-time descriptive and analytical cross-sectional pilot study was conducted. Area Safety Liaisons (n = 330) at University of Texas Health Science Center – Houston were asked to answer a questionnaire regarding their expectations of four workplace indoor air quality indicators i.e., (temperature, relative humidity, carbon dioxide, and carbon monoxide) and if they experienced and reported indoor air quality problems. A chi-square test for independence was used to evaluate associations among the variables of interest. The response rate was 54% (n = 177). Results did not show significant associations between expectation and indoor air quality. However, a greater proportion of Area Safety Liaisons who expected indoor air quality indicators to be better than the established standard experienced greater indoor air quality problems. Similarly, a slightly higher proportion of Area Liaisons who expected indoor air quality indicators to be better than the standard reported greater indoor air quality complaints. ^ The findings indicated that a greater proportion of Area Safety Liaisons with high expectations (conditions that are beyond what is considered normal and acceptable by ASHRAE) experienced greater indoor air quality discomfort. This result suggests a positive association between high expectations and experienced and reported indoor air quality complaints. Future studies may be able to address whether the frequency of complaints and resulting investigations can be reduced through information and education about what are acceptable conditions.^
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Objective: To assess the indoor environment of two different types of dental practices regarding VOCs, PM2.5, and ultrafine particulate concentrations and examine the relationship between specific dental activities and contaminant levels. Method: The indoor environments of two selected dental settings (private practice and community health center) will were assessed in regards to VOCs, PM 2.5, and ultrafine particulate concentrations, as well as other indoor air quality parameters (CO2, CO, temperature, and relative humidity). The sampling duration was four working days for each dental practice. Continuous monitoring and integrated sampling methods were used and number of occupants, frequency, type, and duration of dental procedures or activities recorded. Measurements were compared to indoor air quality standards and guidelines. Results: The private practice had higher CO2, CO, and most VOC concentrations than the community health center, but the community health center had higher PM2.5 and ultrafine PM concentrations. Concentrations of p-dichlorobenzene and PM2.5 exceeded some guidelines. Outdoor concentrations greatly influenced the indoor concentration. There were no significant differences in contaminant levels between the operatory and general area. Indoor concentrations during the working period were not always consistently higher than during the nonworking period. Peaks in particulate matter concentration occurred during root canal and composite procedures.^
Air quality observations by a mobile laboratory aquired on 2015-02-12 to 2015-02-13, Campania region
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Este trabajo presenta un análisis y una metodología para la armonización de inventarios de emisiones utilizados en modelos de calidad del aire.
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1. Introduction 2. Air Quality Modeling system 3. Emission Inventories 4. Applications and Results 5. Conclusions
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Modeling is an essential tool for the development of atmospheric emission abatement measures and air quality plans. Most often these plans are related to urban environments with high emission density and population exposure. However, air quality modeling in urban areas is a rather challenging task. As environmental standards become more stringent (e.g. European Directive 2008/50/EC), more reliable and sophisticated modeling tools are needed to simulate measures and plans that may effectively tackle air quality exceedances, common in large urban areas across Europe, particularly for NO2. This also implies that emission inventories must satisfy a number of conditions such as consistency across the spatial scales involved in the analysis, consistency with the emission inventories used for regulatory purposes and versatility to match the requirements of different air quality and emission projection models. This study reports the modeling activities carried out in Madrid (Spain) highlighting the atmospheric emission inventory development and preparation as an illustrative example of the combination of models and data needed to develop a consistent air quality plan at urban level. These included a series of source apportionment studies to define contributions from the international, national, regional and local sources in order to understand to what extent local authorities can enforce meaningful abatement measures. Moreover, source apportionment studies were conducted in order to define contributions from different sectors and to understand the maximum feasible air quality improvement that can be achieved by reducing emissions from those sectors, thus targeting emission reduction policies to the most relevant activities. Finally, an emission scenario reflecting the effect of such policies was developed and the associated air quality was modeled.
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Background: In recent years, Spain has implemented a number of air quality control measures that are expected to lead to a future reduction in fine particle concentrations and an ensuing positive impact on public health. Objectives: We aimed to assess the impact on mortality attributable to a reduction in fine particle levels in Spain in 2014 in relation to the estimated level for 2007. Methods: To estimate exposure, we constructed fine particle distribution models for Spain for 2007 (reference scenario) and 2014 (projected scenario) with a spatial resolution of 16x16 km2. In a second step, we used the concentration-response functions proposed by cohort studies carried out in Europe (European Study of Cohorts for Air Pollution Effects and Rome longitudinal cohort) and North America (American Cancer Society cohort, Harvard Six Cities study and Canadian national cohort) to calculate the number of attributable annual deaths corresponding to all causes, all non-accidental causes, ischemic heart disease and lung cancer among persons aged over 25 years (2005-2007 mortality rate data). We examined the effect of the Spanish demographic shift in our analysis using 2007 and 2012 population figures. Results: Our model suggested that there would be a mean overall reduction in fine particle levels of 1mg/m3 by 2014. Taking into account 2007 population data, between 8 and 15 all-cause deaths per 100,000 population could be postponed annually by the expected reduction in fine particle levels. For specific subgroups, estimates varied from 10 to 30 deaths for all non-accidental causes, from 1 to 5 for lung cancer, and from 2 to 6 for ischemic heart disease. The expected burden of preventable mortality would be even higher in the future due to the Spanish population growth. Taking into account the population older than 30 years in 2012, the absolute mortality impact estimate would increase approximately by 18%. Conclusions: Effective implementation of air quality measures in Spain, in a scenario with a short-term projection, would amount to an appreciable decline infine particle concentrations, and this, in turn, would lead to notable health-related benefits. Recent European cohort studies strengthen the evidence of an association between long-term exposure to fine particles and health effects, and could enhance the health impact quantification in Europe. Air quality models can contribute to improved assessment of air pollution health impact estimates, particularly in study areas without air pollution monitoring data.
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This study aims to assess the performance or multi-layer canopy parameterizations implemented in the mesoscale WRF model in order to understand their potential contribution to improve the description of energy fluxes and wind fields in the Madrid city. It was found that the Building Energy Model (BEP+BEM) parameterization yielded better results than the bulk standard scheme implemented in the Noah LSM, but very close to those of the Building Energy Parameterization (BEP). The later was deemed as the best option since data requirements and CPU time were smaller. Two annual runs were made to feed the CMAQ chemical-transport model to assess the impact of this feature in routinely air quality modelling activities.
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The present paper describes the advancement and evaluation of air quality-related impacts with the Atmospheric Evaluation and Research Integrated system for Spain (AERIS). In its current version, AERIS is able to provide estimates on the impacts of air quality over human health (PM2.5 and O3), crops and vegetation (O3). The modules that allow quantifying the before mentioned impacts were modeled by applying different approaches (mostly for the European context) present in scientific literature to the conditions of the Iberian Peninsula. This application was supported by reliable data sources, as well as by the good predictive capacity of AERIS for ambient concentrations. For validation purposes, the estimates of AERIS for impacts on human health (change in the statistical life expectancy-PM2.5) and vegetation (loss of wheat crops-O3) were compared against results from the SERCA project and GAINS estimates for two emission scenarios. In general, good results evidenced by reasonable correlation coefficients were obtained, therefore confirming the adequateness of the followed modeling approaches and the quality of AERIS predictions.