Search

In recognition that intraurban exposure gradients may be as large as between-city variations, recent air pollution epidemiologic studies have become increasingly interested in capturing within-city exposure gradients. In addition, because of the rapidly accumulating health data, recent studies also need to handle large study populations distributed over large geographic domains. Even though several modeling approaches have been introduced, a consistent modeling framework capturing within-city exposure variability and applicable to large geographic domains is still missing. To address these needs, we proposed a modeling framework based on the Bayesian Maximum Entropy method that integrates monitoring data and outputs from existing air quality models based on Land Use Regression (LUR) and Chemical Transport Models (CTM). The framework was applied to estimate the yearly average NO2 concentrations over the region of Catalunya in Spain. By jointly accounting for the global scale variability in the concentration from the output of CTM and the intraurban scale variability through LUR model output, the proposed framework outperformed more conventional approaches. [ABSTRACT FROM AUTHOR]

Photochemical mechanisms are a critical module of air quality models. In the past 20 years, several mechanisms have been developed to study the chemistry of the troposphere. This work compares several state-of-the-science photochemical mechanisms (including LCC, CBM-IV, RADM2, EMEP, RACM, SAPRC99, and CACM which have never been compared before in other studies). Concise descriptions of the chemical schemes are included. The main difference among existing mechanisms is the lumping technique used to classify organic compounds into surrogate groups. First, box model calculations are conducted to highlight the features that lead to differences in the modeled behaviors of photochemical pollutants and their precursors. Results indicate that most chemical schemes yield similar ozone concentrations. Nevertheless, there are significant discrepancies, mainly in predicted concentration of HNO3, HO2 and total PAN among model simulations. Finally, the sources of the discrepancy are identified. [Copyright &y& Elsevier]

A statistical model is presented for the determination of hourly global solar radiation from the National Oceanic and Atmospheric Administration advanced very high resolution radiometer (NOAA AVHRR) satellite data, which provide wide coverage together with adequate spatial resolution (around 1.1 km at the nadir). The process is divided into three steps. The first step consists of a cloud detection procedure. The second step determines the cloud index for each point on the satellite image, which is then used for the third step, which is the application of the global solar radiation statistical model. The coefficients for the model are determined by regression from the data obtained from 11 global surface solar radiation measurement stations (pyranometers). Once the coefficients have been determined, a surface interpolation is performed to obtain the entire coefficient field for the area under study, with the objective of applying the model. The estimates obtained from the model were compared with data from another 10 ground radiation measurement stations in Catalonia, Spain. This model was tested for the 11 consecutive months beginning in February of 1998, with an excellent correlation being obtained between the estimate provided by the model and the data from the measurement stations, which resulted in a coefficient of determination of greater than 0.98 in all cases, together with an rmse of between 9.6% and 15.8% and a bias that varied from 9.5% to 1.3%. In southern Catalonia, satellite-estimated and surface-interpolated hourly global solar radiation were of equal quality (rmse of about 3%–15%). In northern Catalonia, where the stations are more sparse, the satellite-estimated values were more accurate (rmse of 7%) than those obtained from interpolation of surface station data (rmse of 11%–16%). [ABSTRACT FROM AUTHOR]

Presents information on the volatile organic compounds (VOC) in Mortorell, Spain which were measured, analyzed and comparison were made to 16 other countries VOCs levels. Information on the study of the VOCs; Results of the study; Discussion on he results.

Wind farms have suffered a spectacular growth in Europe in the last few decades. However, future changes in climate could affect the availability of the wind resource in certain regions. This work assesses the capabilities of a Regional Climate Model (WRF) applied at medium-to-high resolution (10 km, 33 vertical layers) over a particularly complex and vulnerable terrain (North Eastern Iberian Peninsula) to derive mean wind speed and direction for 1981–2000. The model is able to reproduce geographical wind patterns, although it generally overemphasises surface wind intensity when compared to individual observations. Projections of mean wind speed changes for 2031–2050 suggest a decrease in surface wind intensity. Energy density estimations at 60 m agl (typical hub-height) show that the implications of the wind speed weakening could affect the preferential areas for the wind farm locations defined at present. Reductions up to 20% of wind energy density are found already in mid-21st century. Therefore, it is advisable to derive dynamic methodologies to assess the preferential areas for wind farm locations, not only considering past-time wind climatologies, but also considering future climate changes in geographical patterns. Regional Climate Models applied at medium-to-high resolution can be useful for this purpose. [ABSTRACT FROM AUTHOR]

Maximum cross-correlation provides a method to remotely de-ter-mine high-ly re-solved three-dimensional fields of horizontal winds with e-las-tic li-dar through-out large volumes of the planetary boundary layer (PBL). This paper details the technique and shows comparisons between elastic lidar winds, remotely sensed laser Doppler velocimeter (LDV) wind profiles, and radiosonde winds. Radiosonde wind data were acquired at Barcelona, Spain, during the Barcelona Air-Quality Initiative (1992), and the LDV wind data were acquired at Sunland Park, New Mexico during the 1994 Border Area Air-Quality Study. Comparisons show good agreement between the different instruments, and demonstrate the method useful for air pollution management at the local/regional scale. Elastic lidar winds could thus offer insight into aerosol and pollution transport within the PBL. Lidar wind fields might also be used to nudge or improve initialization and evaluation of atmospheric meteorological models. [ABSTRACT FROM AUTHOR]

Accurate continuous measurements of relative humidity (RH) vertical profiles in the lower troposphere have become a significant scientific challenge. In recent years a synergy of various ground-based remote sensing instruments have been successfully used for RH vertical profiling, which has resulted in the improvement of spatial resolution and, in some cases, of the accuracy of the measurement. Some studies have also suggested the use of highresolution model simulations as input datasets into RH vertical profiling techniques. In this paper we apply two synergetic methods for RH profiling, including the synergy of lidar with a microwave radiometer and high-resolution atmospheric modeling. The two methods are employed for RH retrieval between 100 and 6000m with increased spatial resolution, based on datasets from the HygrA-CD (Hygroscopic Aerosols to Cloud Droplets) campaign conducted in Athens, Greece from May to June 2014. RH profiles from synergetic methods are then compared with those retrieved using single instruments or as simulated by high-resolution models. Our proposed technique for RH profiling provides improved statistical agreement with reference to radiosoundings by 27% when the lidar–radiometer (in comparison with radiometer measurements) approach is used and by 15% when a lidar model is used (in comparison with WRF-model simulations). Mean uncertainty of RH due to temperature bias in RH profiling was ~ 4:34% for the lidar–radiometer and ~ 1:22% for the lidar–model methods. However, maximum uncertainty in RH retrievals due to temperature bias showed that lidar-model method is more reliable at heights greater than 2000 m. Overall, our results have demonstrated the capability of both combined methods for daytime measurements in heights between 100 and 6000m when lidar–radiometer or lidar–WRF combined datasets are available. [ABSTRACT FROM AUTHOR]

Background Air pollution exposure has been associated with an increase in mortality rates, but few studies have focused on life expectancy, and most studies had restricted spatial coverage. A limited body of evidence is also suggestive for a beneficial association between residential exposure to greenness and mortality, but the evidence for such an association with life expectancy is still very scarce. Objective To investigate the association of exposure to air pollution and greenness with mortality and life expectancy in Spain. Methods Mortality data from 2148 small areas (average population of 20,750 inhabitants, and median population of 7672 inhabitants) covering Spain for years 2009–2013 were obtained. Average annual levels of PM 10 , PM 2.5 , NO 2 and O 3 were derived from an air quality forecasting system at 4 × 4 km resolution. The normalized difference vegetation index (NDVI) was used to assess greenness in each small area. Air pollution and greenness were linked to standardized mortality rates (SMRs) using Poisson regression and to life expectancy using linear regression. The models were adjusted for socioeconomic status and lung cancer mortality rates (as a proxy for smoking), and accounted for spatial autocorrelation. Results The increase of 5 μg/m 3 in PM 10 , NO 2 and O 3 or of 2 μg/m 3 in PM 2.5 concentration resulted in a loss of life in years of 0.90 (95% credibility interval CI: 0.83, 0.98), 0.13 (95% CI: 0.09, 0.17), 0.20 years (95% CI: 0.16, 0.24) and 0.64 (0.59, 0.70), respectively. Similar associations were found in the SMR analysis, with stronger associations for PM 2.5 and PM 10 , which were associated with an increased mortality risk of 3.7% (95% CI: 3.5%, 4.0%) and 5.7% (95% CI: 5.4%, 6.1%). For greenness, a protective effect on mortality and longer life expectancy was only found in areas with lower socioeconomic status. Conclusions Air pollution concentrations were associated to important reductions in life expectancy. The reduction of air pollution should be a priority for public health. [ABSTRACT FROM AUTHOR]

Maximum cross-correlation provides a method to remotely determine highly resolved three-dimensional fields of horizontal winds with elastic lidar throughout large volumes of the planetary boundary layer (PBL). This paper details the technique and shows comparisons between elastic lidar winds, remotely sensed laser Doppler velocimeter (LDV) wind profiles, and radiosonde winds. Radiosonde wind data were acquired at Barcelona, Spain, during the Barcelona Air-Quality Initiative (1992), and the LDV wind data were acquired at Sunland Park, New Mexico (N.M.), during the Border Area Air-Quality Study (1994). Comparisons show good agreement between the different instruments, and demonstrate the method useful for air pollution management at the local/regional scale. Elastic lidar winds could thus offer insight into aerosol and pollution transport within the PBL. Lidar wind fields might also be used to nudge or improve initialization and evaluation of atmospheric meteorological models. [ABSTRACT FROM AUTHOR]