Geostatistical integration and uncertainty in pollutant concentration surface under preferential sampling

In this paper the focus is on environmental statistics, with the aim of estimating the concentration surface and related uncertainty of an air pollutant. We used air quality data recorded by a network of monitoring stations within a Bayesian framework to overcome difficulties in accounting for prediction uncertainty and to integrate information provided by deterministic models based on emissions meteorology and chemico-physical characteristics of the atmosphere. Several authors have proposed such integration, but all the proposed approaches rely on representativeness and completeness of existing air pollution monitoring networks. We considered the situation in which the spatial process of interest and the sampling locations are not independent. This is known in the literature as the preferential sampling problem, which if ignored in the analysis, can bias geostatistical inferences. We developed a Bayesian geostatistical model to account for preferential sampling with the main interest in statistical integration and uncertainty. We used PM10 data arising from the air quality network of the Environmental Protection Agency of Lombardy Region (Italy) and numerical outputs from the deterministic model. We specified an inhomogeneous Poisson process for the sampling locations intensities and a shared spatial random component model for the dependence between the spatial location of monitors and the pollution surface. We found greater predicted standard deviation differences in areas not properly covered by the air quality network. In conclusion, in this context inferences on prediction uncertainty may be misleading when geostatistical modelling does not take into account preferential sampling

FUTURE EMISSION SCENARIO ANALYSIS OVER ROME URBAN AREA USING COUPLED TRAFFIC ASSIGNMENT AND CHEMICAL TRANSPORT MODELS

The city of Rome is characterized by high ozone, NO2 and PM10 levels claiming for the implementation of emission control strategies to improve the air quality and to decrease the risks of health effects on inhabitants. In this perspective an atmospheric modelling system based on the chemical transport model FARM has been applied for the year 2005 over a nested domain including the metropolitan area. To improve the description of local scale atmospheric circulation characteristics, observational meteorological data are analysed using the Isentropic Analysis package (ISAN). Since urban traffic emissions represent a relevant source of pollutants, hourly emissions coming from this sector have been estimated by means of a traffic assignment model, based on a source-destination approach, coupled with an emission model based on COPERT-3 methodology. The emissions from the other sectors have been derived from the national inventory and then disaggregated at the municipal level. The analysis of model results for the year 2005 against experimental data reveals a good agreement suggesting the use of the modelling system to study the impact on the air quality of different emission control strategies at both regional and urban scales. The 2010 has been considered as the future year base case scenario and the traffic limitation within the Rome urban core has been considered as an emission control action. The impact of this emission scenario has been then analysed by means of a semi-empiric approach: a significant decrease of PM10 and NO2 yearly average concentrations is expected to occur at urban traffic stations while the minimum reduction is expected at urban background and rural stations

Out-of-hospital cardiac arrests in a large metropolitan area : synergistic effect of exposure to air particulates and high temperature

Aims: Air pollution and climate change are intrinsically linked to emerging hazards for global health. High air particulate matter (PM) levels may trigger out-of-hospital cardiac arrest (OHCA). High temperature could act synergistically with PM in determining OHCA. The aim of the present study was to investigate the effect of PM exposure alone, and in combination with temperature, on the risk of OHCA, in a large European metropolitan area with population >4 million. Methods: We evaluated the association between short-term PM exposure, temperature, and the risk of OHCA over a two-year study period, allowing us to investigate 5761 events using a time-stratified case-crossover design combined with a distributed lag non-linear model. Results: Higher risk of OHCA was associated with short-term exposure to PM10. The strongest association was experienced three days before the cardiac event where the estimated change in risk was 1.70% (0.48\u20132.93%) per 10 \ub5g/m3 of PM. The cumulative exposure risk over the lags 0\u20136 was 8.5% (0.0\u201317.9%). We observed a joint effect of PM and temperature in triggering cardiac arrests, with a maximum effect of 14.9% (10.0\u201320.0%) increase, for high levels of PM before the cardiac event, in the presence of high temperature. Conclusion: The present study helps to clarify the controversial role of PM as OHCA determinant. It also highlights the role of increased temperature as a key factor in triggering cardiac events. This evidence suggests that tackling both air pollution and climate change might have a relevant impact in terms of public health

Common Data and Technological Partnership - The Foundation for the Development of Smart Cities - Poznań Case Study

Over the recent years communities have been working towards changing the paradigm of city development into the so-called smart approaches. While various revolutionary solutions have been deployed to make the cities smarter, we believe that a more evolutionary path makes it easier for the cities to change into smart ecosystems. Such an evolutionary path is possible with the right foundation. In this paper we discuss such a foundation that has been making the city of Poznań, Poland, smarter over the last 20 years, and opens opportunities for employing the Citizen Science model of smart city development. This foundation relates to the combination of the creation of a common data space, and the technological partnership with a research and development center and research cyberinfrastructure operator such as the Poznań Supercomputing and Networking Center

Characterization of urban pollution in two cities of the Puglia region in Southern Italy using field measurements and air quality (AQ) model approach

Abstract Passive air sampling (PAS) consisting of polyurethane foam (PUF) disks were deployed simultaneously over four periods of 2–5 months at four locations in urban and sub–urban sites of Bari and San Vito Taranto in Southern Italy. The purpose of the study was to characterize the urban pollution for two groups of semi volatile organic compounds (SVOCs), polychlorinated biphenyls (PCBs) and polycyclic aromatic hydrocarbons (PAHs), by using two different approaches consisting of PAS–PUF and air quality models (Flexible Air quality Regional Model, FARM). The concentrations in the air ranged from 20 to 200 pg m−3 for PCBs and from 5 to 48 ng m−3 for PAHs with the highest concentrations being detected at Bari center. PCB composition was dominated by the 3–Cl congeners (periods 1 and 2) and by 5–Cl (periods 3 and 4). PCB–28 and –37 were the most abundant congeners during the four periods. The PAHs profile was dominated by the 3–ring (70±6)%, with phenanthrene alone accounting for (49±2)%. On a seasonal basis opposite patterns were observed for PCBs and PAHs showing high PCB concentrations during the warm periods, period 3: summer and 2: spring, while PAHs were found during cool periods, period 4: autumn, and 1: winter. The results obtained from the application of the FARM model, during 2010, and limited to period 4 in this study, showed similar estimated levels for PCBs indicating a good performance for PCB modeled concentrations whilst for benzo[b]fluoranthene (B[b]F) the results showed a less better agreement. This study represents one of the few efforts at characterizing PCBs and PAHs compositions in ambient air in southern Italy and also represents one of the preliminary attempts at using PAS–PUF to give more insight into a modeling prediction in Italy. These results also provide useful information for the future development of the FARM model

COVID19 outbreak in Lombardy, Italy: An analysis on the short-term relationship between air pollution, climatic factors and the susceptibility to SARS-CoV-2 infection

Short-term exposure to air pollution, as well as to climate variables have been linked to a higher incidence of respiratory viral diseases. The study aims to assess the short-term influence of air pollution and climate on COVID19 incidence in Lombardy (Italy), during the early stage of the outbreak, before the implementation of the lockdown measures. The daily number of COVID19 cases in Lombardy from February 25th to March 10th, 2020, and the daily average concentrations up to 15 days before the study period of particulate matter (PM10, PM2.5), O3, SO2, and NO2 together with climate variables (temperature, relative humidity – RH%, wind speed, precipitation), were analyzed. A univariable mixed model with a logarithm transformation as link function was applied for each day, from 15 days (lag15) to one day (lag1) before the day of detected cases, to evaluate the effect of each variable. Additionally, change points (Break Points-BP) in the relationship between incident cases and air pollution or climatic factors were estimated. The results did not show a univocal relationship between air quality or climate factors and COVID19 incidence. PM10, PM2.5 and O3 concentrations in the last lags seem to be related to an increased COVID19 incidence, probably due to an increased susceptibility of the host. In addition, low temperature and low wind speed in some lags resulted associated with increased daily COVID19 incidence. The findings observed suggest that these factors, in particular conditions and lags, may increase individual susceptibility to the development of viral infections such as SARS-CoV-2

Long-term exposure to air pollution and COVID-19 incidence: a prospective study of residents in the city of Varese, Northern Italy

OBJECTIVES: To investigate the association between long-term exposure to airborne pollutants and the incidence of SARS-CoV-2 up to March 2021 in a prospective study of residents in Varese city. METHODS: Citizens of Varese aged 6518 years as of 31 December 2019 were linked by residential address to 2018 average annual exposure to outdoor concentrations of PM2.5, PM10, NO2, NO and ozone modelled using the Flexible Air quality Regional Model (FARM) chemical transport model. Citizens were further linked to regional datasets for COVID-19 case ascertainment (positive nasopharyngeal swab specimens) and to define age, sex, living in a residential care home, population density and comorbidities. We estimated rate ratios and additional numbers of cases per 1\u2009\ub5g/m3 increase in air pollutants from single- and bi-pollutant Poisson regression models. RESULTS: The 62 848 residents generated 4408 cases. Yearly average PM2.5 exposure was 12.5\u2009\ub5g/m3. Age, living in a residential care home, history of stroke and medications for diabetes, hypertension and obstructive airway diseases were independently associated with COVID-19. In single-pollutant multivariate models, PM2.5 was associated with a 5.1% increase in the rate of COVID-19 (95%\u2009CI 2.7% to 7.5%), corresponding to 294 additional cases per 100 000 person-years. The association was confirmed in bi-pollutant models; excluding subjects in residential care homes; and further adjusting for area-based indicators of socioeconomic level and use of public transportation. Similar findings were observed for PM10, NO2 and NO. Ozone was associated with a 2% decrease in disease rate, the association being reversed in bi-pollutant models. CONCLUSIONS: Long-term exposure to low levels of air pollutants, especially PM2.5, increased the incidence of COVID-19. The causality warrants confirmation in future studies; meanwhile, government efforts to further reduce air pollution should continue

Benzo[a]pyrene modelling over Italy: comparison with experimental data and source apportionment

Abstract This work describes the extension of the Flexible Air quality Regional Model (FARM) to polycyclic aromatic hydrocarbons (PAHs). Modules accounting for the partitioning of these species between gaseous and particulate phases were inserted in a simplified version of the model and in a more state–of–the–art configuration implementing the SAPRC99 gas–phase chemical mechanism coupled with the aero3 aerosol module. Both versions of FARM were applied over Italy for the year 2005. The analysis of model results was focused on benzo[a]pyrene (B[a]P), which is considered a marker substance for the carcinogenic risk of PAHs. Simulated B[a]P concentrations were compared with observed data, collected at background sites mainly located in Po Valley, and with concentrations produced at continental scale by EMEP/MSC–E model. Higher B[a]P yearly average concentrations were simulated by the national modelling system as a result of different factors: the higher resolution adopted by the national modelling system, the greater Italian emissions estimated by the national inventory and the effects induced by the use of a high resolution topography on meteorological fields and thus on the dispersion of pollutants. The comparison between observed and predicted monthly averaged concentrations evidenced the capability of the two versions of FARM model to capture the seasonal behaviour of B[a]P, characterised by higher values during the winter season due to the large use of wood for residential heating, enhanced by lower dispersion atmospheric conditions. The statistical analysis evidenced, for both versions of the model, a good performance and better indicators than those associated to EMEP/MSC–E simulations. A source apportionment was then carried out using the simplified version of the model, which proved to perform similarly to the full chemistry version but with the advantage to be computationally less expensive. The analysis revealed a significant influence of national sources on B[a]P concentrations, with non–industrial combustion employing wood burning devices being the most important sector. The contribution of the industrial sectors is relevant around major industrial facilities, with the largest absolute contribution in Taranto (above 1 ng m −3 ), where steel industries are the largest individual source of PAHs in the country

impact of emissions meteorology and grid resolution on changes in hms and pahs concentrations between 2005 and 2010 in italy

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Impact of different exposure models and spatial resolution on the long-term effects of air pollution.

Abstract Long-term exposure to air pollution has been related to mortality in several epidemiological studies. The investigations have assessed exposure using various methods achieving different accuracy in predicting air pollutants concentrations. The comparison of the health effects estimates are therefore challenging. This paper aims to compare the effect estimates of the long-term effects of air pollutants (particulate matter with aerodynamic diameter less than 10 μm, PM10, and nitrogen dioxide, NO2) on cause-specific mortality in the Rome Longitudinal Study, using exposure estimates obtained with different models and spatial resolutions. Annual averages of NO2 and PM10 were estimated for the year 2015 in a large portion of the Rome urban area (12 × 12 km2) applying three modelling techniques available at increasing spatial resolution: 1) a chemical transport model (CTM) at 1km resolution; 2) a land-use random forest (LURF) approach at 200m resolution; 3) a micro-scale Lagrangian particle dispersion model (PMSS) taking into account the effect of buildings structure at 4 m resolution with results post processed at different buffer sizes (12, 24, 52, 100 and 200 m). All the exposures were assigned at the residential addresses of 482,259 citizens of Rome 30+ years of age who were enrolled on 2001 and followed-up till 2015. The association between annual exposures and natural-cause, cardiovascular (CVD) and respiratory (RESP) mortality were estimated using Cox proportional hazards models adjusted for individual and area-level confounders. We found different distributions of both NO2 and PM10 concentrations, across models and spatial resolutions. Natural cause and CVD mortality outcomes were all positively associated with NO2 and PM10 regardless of the model and spatial resolution when using a relative scale of the exposure such as the interquartile range (IQR): adjusted Hazard Ratios (HR), and 95% confidence intervals (CI), of natural cause mortality, per IQR increments in the two pollutants, ranged between 1.012 (1.004, 1.021) and 1.018 (1.007, 1.028) for the different NO2 estimates, and between 1.010 (1.000, 1.020) and 1.020 (1.008, 1.031) for PM10, with a tendency of larger effect for lower resolution exposures. The latter was even stronger when a fixed value of 10 μg/m3 is used to calculate HRs. Long-term effects of air pollution on mortality in Rome were consistent across different models for exposure assessment, and different spatial resolutions