Short-term effects of particulate matter constituents on daily hospitalizations and mortality in five South-European cities: Results from the MED-PARTICLES project

BackgroundFew recent studies examined acute effects on health of individual chemical species in the particulate matter (PM) mixture, and most of them have been conducted in North America. Studies in Southern Europe are scarce. The aim of this study is to examine the relationship between particulate matter constituents and daily hospital admissions and mortality in five cities in Southern Europe.MethodsThe study included five cities in Southern Europe, three cities in Spain: Barcelona (2003–2010), Madrid (2007–2008) and Huelva (2003–2010); and two cities in Italy: Rome (2005–2007) and Bologna (2011–2013). A case-crossover design was used to link cardiovascular and respiratory hospital admissions and total, cardiovascular and respiratory mortality with a pre-defined list of 16 PM₁₀ and PM_2.5 constituents. Lags 0 to 2 were examined. City-specific results were combined by random-effects meta-analysis.ResultsMost of the elements studied, namely EC, SO₄^2 −, SiO_2, Ca, Fe, Zn, Cu, Ti, Mn, V and Ni, showed increased percent changes in cardiovascular and/or respiratory hospitalizations, mainly at lags 0 and 1. The percent increase by one interquartile range (IQR) change ranged from 0.69% to 3.29%. After adjustment for total PM levels, only associations for Mn, Zn and Ni remained significant. For mortality, although positive associations were identified (Fe and Ti for total mortality; EC and Mg for cardiovascular mortality; and NO₃⁻ for respiratory mortality) the patterns were less clear.ConclusionsThe associations found in this study reflect that several PM constituents, originating from different sources, may drive previously reported results between PM and hospital admissions in the Mediterranean area.     

The Science of Adaptation: A Framework for Assessment

Mitigation and Adaptation Strategies for Global Change - This paper outlines what is meant by "adaptation" to climate change, and how it might be addressed in the IPCC Assessments. Two...     

A comparative performance evaluation of the AURAMS and CMAQ air-quality modelling systems

A harmonized comparative performance evaluation of A Unified Regional Air-quality Modelling System (AURAMS) v1.3.1b and Community Multiscale Air Quality (CMAQ) v4.6 air-quality modelling systems was conducted on the same North American grid for July 2002 using the same emission inventories, emissions processor, and input meteorology.Comparison of AURAMS- and CMAQ-predicted O₃ concentrations against hourly surface measurement data showed a lower normalized mean bias (NMB) of 20.7% for AURAMS versus 46.4% for CMAQ. However, AURAMS and CMAQ had more similar normalized mean errors (NMEs) of 46.9% and 54.2%, respectively. Both models did similarly well in predicting daily 1-h O₃ maximums; however, AURAMS performed better in calculating daily minimums. CMAQ's poorer performance for O₃ is partly due to its inability to correctly predict nighttime lows.Total PM_2.5 hourly surface concentration was under-predicted by both AURAMS and CMAQ with NMBs of ?10.4% and ?65.2%, respectively. However, as with O₃, both models had similar NMEs of 68.0% and 70.6%, respectively. In general, AURAMS performance was better than CMAQ for all major PM_2.5 species except nitrate and elemental carbon. Both models significantly under-predicted total organic aerosols (TOAs), although the mean AURAMS concentration was over four times larger than CMAQ's. The under-prediction of TOA was partly due to the exclusion of forest-fire emissions. Sea-salt aerosol made up approximately 50.2% of the AURAMS total PM_2.5 surface concentration versus only 6.2% in CMAQ when averaged over all grid cells. When averaged over land cells only, sea-salt still contributed 13.9% to the total PM_2.5 mass in AURAMS versus 2.0% in CMAQ.     

Temporal size distribution and concentration of particles near a major highway

Ultrafine particles (UFP, diameter < 100 nm), as reported in recent findings of toxicological and epidemiological studies, could represent health and environmental risks. Motor vehicle emissions usually constitute the most significant source of UFP in an urban environment. Number, surface and mass concentration of particles were determined at increasing distances from the most important Italian road: the “Autostrada del Sole” A1 highway. Particles in the size range from 0.0059 to 20 μm were measured with a Scanning Mobility Particle Sizer (SMPS) and an Aerodynamic Particle Sizer (APS) spectrometers.The A1 highway was selected because it is characterized by two different traffic conditions: a daily and a weekly traffic. During the weekdays the average traffic flow was about 50 vehicles min^?1 with more than 30% of vehicles being heavy-duty (HD) diesel trucks. The weekly traffic component is characterized by an increased traffic up to approximately 100 vehicles min^?1 during Monday mornings and Friday afternoons because of light-duty vehicles, with substantial reduction of the percentage of HD diesel trucks (typically only 10%).The purpose of this study is the characterization of the A1 highway in terms of evolution of particle size distribution (PSD) and total number concentration at different distances from the highway. This analysis is interesting because Italian traffic presents a higher i) percentage of diesel light-duty vehicles and ii) mean traffic speed in respect to US and Australian traffics. Particle number, surface and mass, exponentially decreases as one moves away from the freeway, whereas UFP number concentration measured at 400 m downwind from the freeway is indistinguishable from upwind background concentration.     

Skill and uncertainty of a regional air quality model ensemble

Recently several regional air quality projects were carried out to support the negotiation under the Clean Air For Europe (CAFE) programme by predicting the impact of emission control policies with an ensemble of models. Within these projects, CITYDELTA and EURODELTA, the fate of air quality at the scale of European cities or that of the European continent was studied using several models. In this article we focus on the results of EURODELTA. The predictive skill of the ensemble of models is described for ozone, nitrogen dioxide and secondary inorganic compounds, and the uncertainty in air quality modelling is examined through the model ensemble spread of concentrations.For ozone daily maxima the ensemble spread origin differs from one region to another. In the neighbourhood of cities or in mountainous areas the spread of predicted values does not span the range of observed data, due to poorly resolved emissions or complex-terrain meteorology. By contrast in Atlantic and North Sea coastal areas the spread of predicted values is found to be larger than the observations. This is attributed to large differences in the boundary conditions used in the different models. For NO₂ daily averages the ensemble spread is generally too small compared with observations. This is because models miss highest values occurring in stagnant meteorology in stable boundary layers near cities. For secondary particulate matter compounds the simulated concentration spread is more balanced, observations falling nearly equiprobably within the ensemble, and the spread originates both from meteorology and aerosol chemistry and thermodynamics.     

Observations and Interpretations from the Canadian Fine Particle Monitoring Program

ABSTRACT

Canadian particle monitoring programs examining PM10, PM2.5, and particle composition have been in operation for over 10 years. Until recently, the measurements were manual/filter-based with 24-hr sample collection varying in frequency from daily to every sixth day, using GrasebyAnderson dichotomous samplers. In the past few years, these monitoring activities have been expanded to include hourly measurements using tapered element oscillating microbalances (TEOMs). This continuous monitoring program started operation focusing on PM_10, but now emphasizes PM_2.5 through the addition of more TEOMs and switching of the inlets of some of the existing units. The data from all of these measurement activities show that there are broad geographical differences and also local- to regional-scale spatial differences in mass and composition of PM2.5. Due to variations in sources, significantly different PM2.5 concentrations are not uncommon within the same city. Comparison of nearby urban and rural sites indicates that 30 and 40% of the PM_2.5 is from local urban sources in Montreal and Toronto, respectively. Hourly PM_2.5 measurements in Toronto suggest that vehicular emissions are an important contributor to urban PM_2.5. There has been a decreasing trend in urban PM_2.5, with annual average concentrations between the 1987–1990 and 1993–1995 periods decreasing by 11 to 39%, depending upon the site. The largest declines were in Montreal and Halifax, and the smallest decline was in Toronto. Comparison of 24-hr TEOM and manual dichotomous sampler PM_2.5 measurements from a site in Toronto indicates that the TEOM results in lower concentrations. The magnitude of this difference is relatively small in the warmer months, averaging about 12%. During the colder months the difference averages about 23%, but can be as large as 50%.     

Process analysis of ozone formation under different weather conditions over the Kanto region of Japan using the MM5/CMAQ modelling system

We have assessed the contributions of individual physical and chemical atmospheric processes on ozone formation under different weather conditions during a typical summer month (August 2005) using the MM5/CMAQ modelling system. We found that the ozone episodes in the Kanto region are dominated by three major patterns, of which Patterns I and II are regular summertime pressure patterns with a 26% and 16% frequency of occurrence, respectively. A process analysis at two typical sites in the Kanto region – one located in the central region of Tokyo and the other located in the rural areas of Kanto – indicates that ozone formation is mainly controlled by advection, vertical diffusion, dry deposition, and chemical processes. The ground-level ozone concentrations are enhanced mainly by the vertical mixing of ozone-rich air from aloft, whereas the dry deposition and chemical processes mainly deplete ozone. By investigating the effects of each process under different weather conditions, we found that the significant decrease in ozone removal due to the chemical and advection processes under conditions of high stagnation is the most important cause of the enhanced levels of ozone in the central region of Tokyo. The results of this study can contribute to a better understanding of ozone formation in the Kanto region, and they may be valuable for local policy makers for further development planning.     

Ensemble and bias-correction techniques for air quality model forecasts of surface O3 and PM2.5 during the TEXAQS-II experiment of 2006

Several air quality forecasting ensembles were created from seven models, running in real-time during the 2006 Texas Air Quality (TEXAQS-II) experiment. These multi-model ensembles incorporated a diverse set of meteorological models, chemical mechanisms, and emission inventories. Evaluation of individual model and ensemble forecasts of surface ozone and particulate matter (PM) was performed using data from 119 EPA AIRNow ozone sites and 38 PM sites during a 50-day period in August and September of 2006. From the original set of models, two new bias-corrected model data sets were built, either by applying a simple running mean average to the past 7 days of data or by a Kalman-Filter approach. From the original and two bias-corrected data sets, three ensembles were created by a simple averaging of the seven models. For further improvements three additional weighted model ensembles were created, where individual model weights were calculated using the singular value decomposition method. All six of the ensembles are compared to the individual models and to each other in terms of root mean square error, correlation, and contingency and probabilistic statistics. In most cases, each of the ensembles show improved skill compared to the best of the individual models. The over all best ensemble technique was found to be the combination of Kalman-Filtering and weighted averaging. PM_2.5 aerosol ensembles demonstrated significant improvement gains, mostly because the original model's skill was very low.     

Back-extrapolation of estimates of exposure from current land-use regression models

Land use regression has been used in epidemiologic studies to estimate long-term exposure to air pollution within cities. The models are often developed toward the end of the study using recent air pollution data. Given that there may be spatially-dependent temporal trends in urban air pollution and that there is interest for epidemiologists in assessing period-specific exposures, especially early-life exposure, methods are required to extrapolate these models back in time. We present herein three new methods to back-extrapolate land use regression models. During three two-week periods in 2005–2006, we monitored nitrogen dioxide (NO₂) at about 130 locations in Montreal, Quebec, and then developed a land-use regression (LUR) model. Our three extrapolation methods entailed multiplying the predicted concentrations of NO₂ by the ratio of past estimates of concentrations from fixed-site monitors, such that they reflected the change in the spatial structure of NO₂ from measurements at fixed-site monitors. The specific methods depended on the availability of land use and traffic-related data, and we back-extrapolated the LUR model to 10 and 20 years into the past. We then applied these estimates to residential information from subjects enrolled in a case–control study of postmenopausal breast cancer that was conducted in 1996.Observed and predicted concentrations of NO₂ in Montreal decreased and were correlated in time. The estimated concentrations using the three extrapolation methods had similar distributions, except that one method yielded slightly lower values. The spatial distributions varied slightly between methods. In the analysis of the breast cancer study, the odds ratios were insensitive to the method but varied with time: for a 5 ppb increase in NO₂ using the 2006 LUR the odds ratio (OR) was about 1.4 and the ORs in predicted past concentrations of NO₂ varied (OR～1.2 for 1985 and OR～1.3–1.5 for 1996). Thus, the ORs per unit exposure increased with time as the range and variance of the spatial distributions decreased, and this is due partly to the regression coefficient being approximately inversely proportional to the variance of exposure. Changing spatial variability complicates interpretation and this may have important implications for the management of risk. Further studies are needed to estimate the accuracy of the different methods.     

基于图像感兴趣区域融合特征的PM2.5浓度预测方法

通过图像预测PM_2.5浓度的准确性,在很大程度上取决于模型所选用的特征参数。为丰富特征参数的表达,设计了一种基于图像传统特征与深度特征充分融合的PM_2.5浓度预测方法。首先,根据不同PM_2.5浓度下的成像差异,选定图像感兴趣区域,解决图像尺寸过大导致的模型运算效率较低问题。然后,针对所选取的局部图像,利用传统图像处理方法手动设计并提取图像浅表视觉特征,同时利用卷积神经网络自动提取图像深层语义特征。最后,将两种特征融合,交由卷积神经网络的全连接层实现对PM_2.5浓度的回归预测。预测误差比对结果显示,相比使用单种特征,使用融合特征能够有效提高模型的预测性能。