Integrating traffic pollution dispersion into spatiotemporal NO2 prediction

Accurately predicting ambient NO2 concentrations has great public health importance, as traffic-related air pollution is of major concern in urban areas. In this study, we present a novel approach incorporating traffic contribution to NO2 prediction in a fine-scale spatiotemporal model. We used nationally available traffic estimate dataset in a scalable dispersion model, Research LINE source dispersion model (RLINE). RLINE estimates then served as an additional input for a validated spatiotemporal pollution modeling approach. Our analysis uses measurement data collected by the Multi-Ethnic Study of Atherosclerosis and Air Pollution in the greater Los Angeles area between 2006 and 2009. We predicted road-type-specific annual average daily traffic (AADT) on road segments via national-level spatial regression models with nearest-neighbor Gaussian processes (spNNGP); the spNNGP models were trained based on over half a million point-level traffic volume measurements nationwide. AADT estimates on all highways were combined with meteorological data in RLINE models. We evaluated two strategies to integrate RLINE estimates into spatiotemporal NO2 models: 1) incorporating RLINE estimates as a space-only covariate and, 2) as a spatiotemporal covariate. The results showed that integrating the RLINE estimates as a space-only covariate improved overall cross-validation R2 from 0.83 to 0.84, and root mean squared error (RMSE) from 3.58 to 3.48 ppb. Incorporating the estimates as a spatiotemporal covariate resulted in similar model improvement. The improvement of our spatiotemporal model was more profound in roadside monitors alongside highways, with R2 increasing from 0.56 to 0.66 and RMSE decreasing from 3.52 to 3.11 ppb. The observed improvement indicates that the RLINE estimates enhanced the model's predictive capabilities for roadside NO2 concentration gradients even after considering a comprehensive list of geographic covariates including the distance to roads. Our proposed modeling framework can be generalized to improve high-resolution prediction of NO2 exposure - especially near major roads in the U.S.

Risk assessment for the optimization of the grid of a telemetric network monitoring system

The goal of this work was to develop a methodology for risk assessment in case of an accident originating from a nuclear power plant, and consequently, to improve the relevant radiation monitoring network. In specific, the study involved risk estimation in Greece from a transboundary nuclear power plant accident. The tool employed was JRODOS (Java-based Real-time Decision Support), which is a system for off-site emergency management of radioactive material in the environment. This tool, widely used to generate and study scenarios for nuclear accidents worldwide, provides valuable insight to facilitate emergency preparedness and response. The probability of the plume arriving at numerous regions within the country was calculated, along with the maximum dose rates in case of transport. A risk assessment was performed, and geographical regions were prioritized in terms of risk-based environmental radioactivity burden. A total of 29 administrative districts were identified as low to medium-risk regions. Acquired results were used to determine the optimal spatial distribution of detectors for upgrading the existing monitoring network of environmental radioactivity.

Cardiorespiratory diseases in an industrialized area: a retrospective population-based cohort study

BACKGROUND

Atmospheric pollution has been recognized as the greatest environmental threat to human health. The population of the Venafro Valley, southern Italy, is exposed to emissions from a Waste-To-Energy (WTE) and a cement plant and potentially also to another WTE located in the neighboring region of Lazio; also, the vehicular atmospheric pollution situation is critical. In order to assess the environmental health risk of residents in eight municipalities of the Venafro Valley, a retrospective residential cohort study during 2006-2019 was carried out.

METHODS

Four exposure classes were defined by natural-break method, using a dispersion map of nitrogen dioxides (chosen as proxy of industrial pollution). The association between the industrial pollution and cause-specific mortality/morbidity of the cohort was calculated using the Hazard Ratio (HR) through a multiple time-dependent and sex-specific Cox regression adjusting for age, proximity to main roads and socio-economic deprivation index.

RESULTS

Results showed, for both sexes, mortality and morbidity excesses in the most exposed class for diseases of the circulatory system and some signals for respiratory diseases. Particularly, mortality excesses in both sexes in class 3 for diseases of the circulatory system [men: HR = 1.37 (1.04-1.79); women: HR = 1.27 (1.01-1.60)] and for cerebrovascular diseases [men: HR = 2.50 (1.44-4.35); women: HR = 1.41 (0.92-2.17)] were observed and confirmed by morbidity analyses. Mortality excesses for heart diseases for both sexes [men-class 3: HR = 1.32 (0.93-1.87); men-class 4: HR = 1.95 (0.99-3.85); women-class 3: HR = 1.49 (1.10-2.04)] and for acute respiratory diseases among women [HR = 2.31 (0.67-8.00)] were observed. Morbidity excesses in both sexes for ischemic heart diseases [men-class 3: HR = 1.24 (0.96-1.61); women-class 4: HR = 2.04 (1.04-4.02)] and in class 4 only among men for respiratory diseases [HR = 1.43 (0.88-2.31)] were also found.

CONCLUSIONS

The present study provides several not-negligible signals indicating mitigation actions and deserve further investigations. For future studies, the authors recommend enriching the exposure and lifestyle profile using tools such as questionnaires and human biomonitoring.

Health and economic benefits of heavy-duty diesel truck emission control policies in Beijing

PM2.5 emissions from heavy-duty diesel trucks (HDDTs) have a significant impact on air quality, human health, and climate change, and seriously threaten the UN Sustainable Development Goals. Globally, a series of emission control measures have been implemented to reduce pollution emissions from HDDTs. Current studies assessing the impact of these measures on air quality and human health have mainly used coarse-grained emission data as input to dispersion model, resulting in the inability to capture the spatiotemporal variability of pollutant concentrations and tending to increase the uncertainty of health impact assessment results. In this study, we quantified the impact of pollution control policies for HDDTs in Beijing on PM2.5 concentrations, human health, and economic losses by integrating policy scenario analysis, pollution dispersion simulation, public health impact and economic benefit assessment models, supported by high spatiotemporal resolution emission data from HDDTs. The results show that PM2.5 concentrations from HDDTs exhibit significant spatial aggregation characteristics, with the intensity of aggregation at night being about twice as high as that during the day. The emission hotspots are mainly concentrated in the sixth, fifth and fourth rings and major highways. Compared to the "business as usual" scenario in 2018, the current policy of updating the fuel standard to China VI and the emission standard to China 6 can reduce PM2.5 concentrations by 96.72%, thereby avoiding 612 premature deaths, which is equivalent to obtaining economic benefits of 1.65 billion CNY. This study further emphasizes the importance of high spatiotemporal resolution emission data during traffic dispersion modeling. The results can help improve the understanding of the effectiveness of emission reduction measures for HDDTs from a health benefit perspective.

Disinfection efficiency prediction under dynamic conditions: Application to peracetic acid disinfection of wastewater

In this work, a mechanistic dynamic model of continuous flow peracetic acid (PAA) disinfection was developed, calibrated and validated, assuming E. coli as indicator microorganism. The model was conceived as a 1-dimensional dispersion model integrating PAA first order decay and E. coli inactivation rate. Lab-scale batch experiments of PAA decay and E. coli inactivation experiments were performed to calibrate corresponding kinetic models. In each sample, conventional wastewater quality parameters were monitored. A PAA pilot reactor was set up to perform both tracer studies, for dispersion model calibration, and continuous flow disinfection experiments, to validate the integration of hydraulics and kinetics models, under both stationary and dynamic conditions. Linear regression models were calibrated to predict hydrodynamic dispersion, given the flow rate, and PAA decay parameters, given effluent quality and PAA dosage. Successful validation of the PAA disinfection model proved the importance of (i) considering the disinfection process as a dynamic system and (ii) integrating real-time estimation of process disturbances, being the initial E. coli concentration and the impact of effluent quality and PAA dosage on PAA decay kinetics. Importantly, novel inactivation models were proposed, as two different modifications of a literature model for thermal inactivation. These models are suitable for dynamic simulation of Eulerian models and can describe the typical triphasic behavior of inactivation kinetics.

Lowering mortality risks in urban areas by containing atmospheric pollution

OBJECTIVES

Although studies collectively examining the traffic and residential heat pollutant emissions are abundant, research investigations dedicated to Cyprus are scarce. This investigation has simulated the levels of air pollutants, namely, CO, NO_x, PM_2.5, and PM₁₀ and reconciled them with actual air quality measurements in Nicosia, Cyprus, during a 9-month period at an hourly resolution. To this end, several scenarios and cases were formulated to tackle emissions and minimise human mortality risks in the city.

METHODS

The GRAL dispersion model was used to project pollution levels. Nine different traffic scenarios were devised to estimate variations in concentration of PM_2.5 and NO_x under various policies, such as banning diesel passenger vehicles (PV), light duty vehicles (LDV), non-Euro 6 standards vehicles, stringent speed limits and a ubiquitous roll-out of electric passenger vehicles. Moreover, 4 distinct cases were analysed to year 2030 considering a fluctuation in traffic of ±20% whereas all vehicles conform to Euro 6 standards. Three additional policies examined the prohibition of diesel PV and LDV, 80% electric PV and outlawing fireplaces. Drawing on the findings of these scenarios and cases, the total cardiovascular and respiratory mortality rates at the capital of Cyprus, Nicosia, were deduced.

RESULTS

The most promising scenario in terms of curbing emissions was to ban non-Euro 6 vehicles and diesel PV and LDV which could contain average NO_x concentration, in Nicosia, from 52.9 μg/m³ to 15.0 μg/m³. If this policy were to be implemented, it could have saved 70% of the premature deaths tied to NO_x emissions. For particulate matter, banning fireplaces and abandoning non-Euro 6 vehicles could lower average concentrations from 18.3 μg/m³ to 13.1 μg/m³, saving at least 30% of the people poised to lose their lives from particulate matter risks.

CONCLUSION

Traffic and residential heat policies are not easy to implement. However, our study has demonstrated that the most effective policies for curbing NO_x emissions would be to ensure that all vehicles abide with the Euro 6 standards and, concurrently, ban diesel passenger and light duty vehicles. Lastly, phasing out domestic fireplaces appears to be the most promising solution for containing particulate matter, in 2030.

Health risk assessment of gaseous elemental mercury (GEM) in Mexico City

Emissions of gaseous elemental mercury (GEM or Hg⁰) from different sources in urban areas are important subjects for environmental investigations. In this study, atmospheric Hg measurements were conducted to investigate air pollution in the urban environment by carrying out several mobile surveys in Mexico City. This work presents atmospheric concentrations of GEM in terms of diurnal variation trends and comparisons with criteria for pollutant concentrations such as CO, SO₂, NO₂, PM_2.5, and PM₁₀. The concentration of GEM was measured during the pre-rainy period by using a high-resolution active air sampler, the Lumex RA 915 M mercury analyzer. In comparison with those for other cities worldwide, the GEM concentrations were similar or slightly elevated, and they ranged from 0.20 to 30.23 ng m^-3. However, the GEM concentration was significantly lower than those in contaminated areas, such as fluorescent lamp factory locations and gold mining zones. The GEM concentrations recorded in Mexico City did not exceed the WHO atmospheric limit of 200 ng m^-3. We performed statistical correlation analysis which suggests equivalent sources between Hg and other atmospheric pollutants, mainly NO₂ and SO₂, emitted from urban combustion and industrial plants. The atmospheric Hg emissions are basically controlled by sunlight radiation, as well as having a direct relationship with meteorological parameters. The area of the city studied herein is characterized by high traffic density, cement production, and municipal solid waste (MSW) treatment, which constantly release GEM into the atmosphere. In this study, we included the simulation with the HYSPLIT dispersion model from three potential areas of GEM release. Emissions from industrial corridors and volcanic plumes localized outside the urban area contribute to the pollution of Mexico City and mainly affect the northern area during specific periods and climate conditions. Using the USEPA model, we assessed the human health risk resulting from exposure to inhaled GEM among residents of Mexico City. The results of the health risk assessment indicated no significant noncarcinogenic risk (hazard quotient (HQ) < 1) or consequent adverse effects for children and adults living in the sampling area over the study period. GEM emissions inventory data is necessary to improve our knowledge about the Hg contribution and effect in urban megacity areas with the objective to develop public safe policy and implementing the Minamata Convention.

Designing local air pollution policies focusing on mobility and heating to avoid a targeted number of pollution-related deaths: Forward and backward approaches combining air pollution modeling, health impact assessment and cost-benefit analysis

CONTEXT

Policies aiming at decreasing air pollutants (e.g., fine particulate matter, PM_2.5) are often designed without targeting an explicit health benefit nor carrying out cost-benefit analyses.

METHODS

We developed a transdisciplinary backward and forward approach at the conurbation level: from health objectives set by local decision-makers, we estimated which reductions in PM_2.5 exposures and emissions would allow to reach them, and identified urban policies leading to these reductions (backward approach). We finally conducted health impact and cost-benefit analyses of these policies (forward approach). The policies were related to the most emitting sectors in the considered area (Grenoble, France), wood heating and transport sectors. The forward approach also considered the health impact and co-benefits of these policies related to changes in physical activity and CO₂ emissions.

FINDINGS

Decision-makers set three health targets, corresponding to decreases by 33% to 67% in PM_2.5-attributable mortality in 2030, compared to 2016. A decrease by 42% in PM_2.5 exposure (from 13.9 µg/m³) was required to reach the decrease by 67% in PM_2.5-attributable mortality. For each Euro invested, the total benefit was about 30€ for policies focusing on wood heating, and 1 to 68€ for traffic policies. Acting on a single sector was not enough to attain a 67% decrease in PM_2.5-attributable mortality. This target could be achieved by replacing all inefficient wood heating equipment by low-emission pellet stoves and reducing by 36% the traffic of private motorized vehicles. This would require to increase the share of active modes (walking, biking…), inducing increases in physical activity and additional health benefits beyond the initial target. Annual net benefits were between €484 and €629 per capita for policies with report on active modes, compared to between €162 and €270 without.

CONCLUSIONS

Urban policies strongly reducing air pollution-attributable mortality can be identified by our approach. Such policies can be cost-efficient.

Integration of air quality model with GIS for the monitoring of PM2.5 from local primary emission at a rural site

Local primary emissions of air pollutants are responsible for public health, decreasing productivity, and cultural activities in local residential areas. In this study, an integrated air quality observation and modeling system with a geographical information system (GIS) was developed to characterize the air pollution caused by local primary emission sources. This integrated system could provide air quality monitoring, data analysis, and visualization results that reflect air pollutant concentration data in a study area containing a local rural village (LRV) and an asphalt manufacturing facility (AMF). Additionally, the model was used to estimate the contributions of air quality from an emission source at the receptor and determine the control factor for the emission rate or meteorological changes. From the forward and backward modeling results, we found that the concentrations of particulate matter smaller than 2.5 μm (PM_2.5) concentrations in the village were affected by the unique meteorological and emission conditions. The PM_2.5 concentration was significantly increased for the cases with a slow wind speed of 1 m/s or high wind speed of 3 m/s, with an emission rate of 10 g/s. The contribution of AMF emissions was explained by contribution factor analysis. During the study period of December 2014-December 2015, the incoming contribution of PM_2.5 at the LRV measurement station was approximately 47.6%. These results suggest that the proposed method can be useful for understanding adverse air quality conditions and estimating the emissions of air pollutants from primary sources for local environmental and public health authorities.

Overviewing the air quality models on air pollution in Sichuan Basin, China

Most developing countries in the world face the common challenges of reducing air pollution and advancing the process of sustainable development, especially in China. Air pollution research is a complex system and one of the main methods is through numerical simulation. The air quality model is an important technical method, it allows researchers to better analyze air pollutants in different regions. In addition, the SCB is a high-humidity and foggy area, and the concentration of atmospheric pollutants is always high. However, research on this region, one of the four most polluted regions in China, is still lacking. Reviewing the application of air quality models in the SCB air pollution has not been reported thoroughly. To fill these gaps, this review provides a comprehensive narration about i) The status of air pollution in SCB; ii) The application of air quality models in SCB; iii) The problems and application prospects of air quality models in the research of air pollution. This paper may provide a theoretical reference for the prevention and control of air pollution in the SCB and other heavily polluted areas in China and give some¹inspirations for air pollution forecast in other countries with complex terrain.

A hybrid approach to predict daily NO2 concentrations at city block scale

Estimating the ambient concentration of nitrogen dioxide (NO2) is challenging because NO2 generated by local fossil fuel combustion varies greatly in concentration across space and time. This study demonstrates an integrated hybrid approach combining dispersion modeling and land use regression (LUR) to predict daily NO2 concentrations at a high spatial resolution (e.g., 50 m) in the New York tri-state area. The daily concentration of traffic-related NO2 was estimated at the Environmental Protection Agency's NO2 monitoring sites in the study area for the years 2015-2017, using the Research LINE source (R-LINE) model with inputs of traffic data provided by the Highway Performance and Management System and meteorological data provided by the NOAA Integrated Surface Database. We used the R-LINE-predicted daily concentrations of NO2 to build mixed-effects regression models, including additional variables representing land use features, geographic characteristics, weather, and other predictors. The mixed model was selected by the Elastic Net method. Each model's performance was evaluated using the out-of-sample coefficient of determination (R2) and the square root of mean squared error (RMSE) from ten-fold cross-validation (CV). The mixed model showed a good prediction performance (CV R2: 0.75-0.79, RMSE: 3.9-4.0 ppb). R-LINE outputs improved the overall, spatial, and temporal CV R2 by 10.0%, 18.9% and 7.7% respectively. Given the output of R-LINE is point-based and has a flexible spatial resolution, this hybrid approach allows prediction of daily NO2 at an extremely high spatial resolution such as city blocks.

Comparison of associations between mortality and air pollution exposure estimated with a hybrid, a land-use regression and a dispersion model

INTRODUCTION

To characterize air pollution exposure at a fine spatial scale, different exposure assessment methods have been applied. Comparison of associations with health from different exposure methods are scarce. The aim of this study was to evaluate associations of air pollution based on hybrid, land-use regression (LUR) and dispersion models with natural cause and cause-specific mortality.

METHODS

We followed a Dutch national cohort of approximately 10.5 million adults aged 29+ years from 2008 until 2012. We used Cox proportional hazard models with age as underlying time scale and adjusted for several potential individual and area-level socio-economic status confounders to evaluate associations of annual average residential NO2, PM2.5 and BC exposure estimates based on two stochastic models (Dutch LUR, European-wide hybrid) and deterministic Dutch dispersion models.

RESULTS

Spatial variability of PM2.5 and BC exposure was smaller for LUR compared to hybrid and dispersion models. NO2 exposure variability was similar for the three methods. Pearson correlations between hybrid, LUR and dispersion modeled NO2 and BC ranged from 0.72 to 0.83; correlations for PM2.5 were slightly lower (0.61-0.72). In general, all three models showed stronger associations of air pollutants with respiratory disease and lung cancer mortality than with natural cause and cardiovascular disease mortality. The strength of the associations differed between the three exposure models. Associations of air pollutants estimated by LUR were generally weaker compared to associations of air pollutants estimated by hybrid and dispersion models. For natural cause mortality, we found a hazard ratio (HR) of 1.030 (95% confidence interval (CI): 1.019, 1.041) per 10 µg/m3 for hybrid modeled NO2, a HR of 1.003 (95% CI: 0.993, 1.013) per 10 µg/m3 for LUR modeled NO2 and a HR of 1.015 (95% CI: 1.005, 1.024) per 10 µg/m3 for dispersion modeled NO2.

CONCLUSION

Air pollution was positively associated with natural cause and cause-specific mortality, but the strength of the associations differed between the three exposure models. Our study documents that the selected exposure model may contribute to heterogeneity in effect estimates of associations between air pollution and health.

Integrated model for methane emission and dispersion assessment from landfills: A case study of Ho Chi Minh City, Vietnam

Methane is considered to be one of the main causes of global warming. Quantifying methane emissions from landfills is the subject of many studies, especially emphasizing the role of two parameters: methane generation potential capacity (L₀), methane generation rate (k). In this study, we propose a system of integrated environmental information and mathematical model named EnLandFill (ENvironmental information - model integrated system for air emission and dispersion estimation from LandFill) that allows calculation L₀ from database and experimentally to determine optimal k. To perform experimental calculations, meteorological data were extracted from the WRF model and verified with real measurements. The novelty of this study lies in the inferred database system, the math model bank, especially the dispersion model, taking note account the complex topography, meteorological factors that change by the hour. EnLandFill was applied to Phuoc Hiep Landfill (PHLF) in Ho Chi Minh City as a case study, the results have identified the amount of methane released that is equal to 44,094,697.88 m³/year in 2019, but EnLandFill is designed to be general, applicable to other landfill entities.

Determination of ammonia and hydrogen sulfide emissions from a commercial dairy farm with an exercise yard and the health-related impact for residents

Airborne emissions from concentrated animal feeding operations (CAFOs) have the potential to pose a risk to human health and the environment. Here, we present an assessment of the emission, dispersion, and health-related impact of ammonia and hydrogen sulfide emitted from a 300-head, full-scale dairy farm with an exercise yard in Beijing, China. By monitoring the referred gas emissions with a dynamic flux chamber for seven consecutive days, we examined their emission rates. An annual hourly emission time series was constructed on the basis of the measured emission rates and a release modification model. The health risk of ammonia and hydrogen sulfide emissions around the dairy farm was then determined using atmospheric dispersion modeling and exposure risk assessment. The body mass-related mean emission factors of ammonia and hydrogen sulfide were 2.13 kg a^-1 AU^-1 and 24.9 g a^-1 AU^-1, respectively (one animal unit (AU) is equivalent to 500 kg body mass). A log-normal distribution fitted well to ammonia emission rates. Contour lines of predicted hourly mean concentrations of ammonia and hydrogen sulfide were mainly driven by the meteorological conditions. The concentrations of ammonia and hydrogen sulfide at the fence line were below 10 μg m^-3 and 0.04 μg m^-3, respectively, and were 2-3 orders of magnitude lower than the current Chinese air quality standards for such pollutants. Moreover, the cumulative non-carcinogenic risks (HI) of ammonia and hydrogen sulfide were 4 orders of magnitudes lower than the acceptable risk levels (HI = 1). Considering a health risk criterion of 1E-4, the maximum distance from the farm fence line to meet this criterion was nearly 1000 m towards north-northeast. The encompassed area of the contour lines of the ambient concentration of ammonia is much larger than that of hydrogen sulfide. However, the contour lines of the ammonia health risk are analogous to those of hydrogen sulfide. In general, the ammonia and hydrogen sulfide emissions from the dairy farm are unlikely to cause any health risks for the population living in the neighborhood.

A multi-city air pollution population exposure study: Combined use of chemical-transport and random-Forest models with dynamic population data

Cities are severely affected by air pollution. Local emissions and urban structures can produce large spatial heterogeneities. We aim to improve the estimation of NO₂, O₃, PM_2.5 and PM₁₀ concentrations in 6 Italian metropolitan areas, using chemical-transport and machine learning models, and to assess the effect on population exposure by using information on urban population mobility. Three years (2013-2015) of simulations were performed by the Chemical-Transport Model (CTM) FARM, at 1 km resolution, fed by boundary conditions provided by national-scale simulations, local emission inventories and meteorological fields. A downscaling of daily air pollutants at higher resolution (200 m) was then carried out by means of a machine learning Random-Forest (RF) model, considering CTM and spatial-temporal predictors, such as population, land-use, surface greenness and vehicular traffic, as input. RF achieved mean cross-validation (CV) R² of 0.59, 0.72, 0.76 and 0.75 for NO₂, PM₁₀, PM_2.5 and O₃, respectively, improving results from CTM alone. Mean concentration fields exhibited clear geographical gradients caused by climate conditions, local emission sources and photochemical processes. Time series of population weighted exposure (PWE) were estimated for two months of the year 2015 and for five cities, by combining population mobility data (derived from mobile phone traffic volumes data), and concentration levels from the RF model. PWE_RF metric better approximated the observed concentrations compared with the predictions from either CTM alone or CTM and RF combined, especially for pollutants exhibiting strong spatial gradients, such as NO₂. 50% of the population was estimated to be exposed to NO₂ concentrations between 12 and 38 μg/m³ and PM₁₀ between 20 and 35 μg/m³. This work supports the potential of machine learning methods in predicting air pollutant levels in urban areas at high spatial and temporal resolutions.

Implementation of various hypothetical low emission zone scenarios in Greater Paris: Assessment of fine-scale reduction in exposure and expected health benefits

OBJECTIVES

Literature assessing the effects of policies aimed at reducing traffic-related air pollution is scarce. The aims of this study were to evaluate the expected impacts, in terms of air quality and health effects, of various hypothetical low-emission zone (LEZ) scenarios in Greater Paris for a planned intervention in 2018/2019 which combine two different perimeters and two levels of vehicles ban, and to assess those impacts according to the socioeconomic status (SES) of the population.

METHODS

We evaluated the effects of four hypothetical LEZ scenarios on various stages of the full-chain model, more specifically, road traffic modelling (traffic flow, type of vehicles and related number of kilometers driven), emissions, fine scale PM_2.5 and NO₂ concentrations, related resident population exposure, and health effects. We computed the overall benefits of expected air pollution improvements in terms of preventable deaths and a decrease in new cases of the following three major chronic diseases: ischemic heart diseases in adults, asthma in children and low weight in full-term newborns.

RESULTS

The most stringent LEZ scenario would lower the maximum level of exposure from 55 μg/m³ to 42 μg/m³ in Paris. In one year, this scenario would help prevent: 340 deaths (-0.6%) representing 114,300 life years gained, 170 low-weight full-term births (-4.9%), 130 new cases of ischemic heart disease (IHD) (-1.8%) and 2930 new cases of asthma (-3.0%) among 9.4 million residents. Residents outside the LEZ would also benefit from this scenario. Results indicated that the intervention could contribute to increasing inequalities. The comparison of scenarios underlined the value of extending the LEZ to include a wider zone (including 80 more municipalities surrounding Paris). This would lead to a more equitable spread of the benefits over the population.

CONCLUSION

Traffic control policies such as LEZ are difficult to accept for some categories of commuters and economic stakeholders. As of June 2019, the concertation process for the proposed Paris LEZ is still ongoing. This work provides authorities with detailed analyses of the options for this measure as well as information on related implications. It will help decision makers prioritize which preventive measures to introduce.

Drifting away. Seawater survival and stochastic transport of the invasive Carpobrotus edulis

Coastal areas are vulnerable and fluctuating habitats that include highly valuable spaces for habitat and species conservation and, at the same time, they are among the most invaded ecosystems worldwide. Occupying large areas within Mediterranean-climate coastlines, the "ecosystem engineer" Carpobrotus edulis appears as a menace for coastal biodiversity and ecosystem services. By combining the observation, current distribution, glasshouse experiment, and dispersion modeling, we aim to achieve a better understanding of the successful invasion process and potential dispersion patterns of C. edulis. We analyzed the response of plant propagules (seeds and plant fragments) to seawater immersion during increasing periods of time (up to 144 h). After 2 months of growth, plant fragments showed a total survival rate (100%) indicating high tolerance to salinity. During this time, fragment length was increased (up to 60%) and root length was higher than control in all cases. Also, immersed fragments consistently accumulated more biomass than control fragments. After two months of growth, photosynthetic parameters (F_v'/F_m', Φ_NO, and Φ_II) remained stable compared to control fragments. Physiologically, osmolyte and pigment content did not evidence significant changes regardless of immersion time. Based on the capacity of propagules to survive seawater immersion, we modeled the potential transport of C. edulis by combining an oceanic model (ROMS-AGRIF) with a particle-tracking model. Results indicated that propagules may travel variable distances maintaining physiological viability. Our model suggested that short-scale circulation would be the dominant process, however, long-scale circulation of propagules may be successfully accomplished in <6 days. Furthermore, under optimal conditions (southerly winds dominance), propagules may even travel large distances (250 km alongshore). Modeling transport processes, in combination with the dynamics of introduction and expansion, will contribute to a better understanding of the invasive mechanisms of C. edulis and, consequently, to design preventive strategies to reduce the impact of plant invasion.

Mortality and hospitalization associated to emissions of a coal power plant: A population-based cohort study

BACKGROUND

Coal-fired thermal power plants represent a significant source of air pollutants, especially sulfur dioxide (SO₂) that has been associated with an increased risk of mortality and morbidity for respiratory and cardiovascular disease. A coal power plant in Vado Ligure (Italy) (CPP_VL) started in 1970 was stopped in 2014 by the Prosecutor's Office on the grounds of environmental and health culpable disaster.

OBJECTIVE

To investigate the association between the exposure of residents to atmospheric pollutants emitted by CPP_VL and the risk of mortality and hospitalization, considering both cancer and non-cancer causes in a population-based cohort study.

METHODS

SO₂ and nitrogen oxides (NO_x), estimated using the ABLE-MOLOCH-ADMS-Urban dispersion model, were selected as representative surrogates of exposure to CPP_VL emissions (SO₂-CPP_VL) and cumulative emissions from other sources of pollution (NO_x-MS), respectively. The relationship between each health outcome and categories of exposure to SO₂-CPP_VL was estimated by the Hazard Ratio (HR) using multiple sex-specific Cox regression models, adjusted for age, exposure to NO_x-MS, and socio-economic deprivation index using SO₂-CPP_VL first quartile as a reference.

RESULTS

144,019 individuals were recruited (follow-up 2001-2013). An excess of mortality was found for all natural causes (men: 1.49; 95% CI 1.38-1.60; women: 1.49; 95% CI 1.39-1.59), diseases of the circulatory system (men: 1.41; 95% CI 1.24-1.56; women: 1.59; 95% CI 1.44-1.77), of the respiratory system (men: 1.90; 95% CI 1.47-2.45; women: 1.62; 95% CI 1.25-2.09), and of the nervous system and sense organs (men: 1.34; 95% CI 0.97-1.86; women: 1.38; 95% CI 1.03-1.83), and in men for trachea, bronchus, and lung cancers (1.59; 95% CI 1.26-2.00). Results of hospitalization analysis were consistent with those of mortality.

CONCLUSION

Results obtained, also when considering multiple sources of exposure, indicate that exposure to CPP emissions represents a risk factor for selected health outcomes as well as the urgently adoption of primary prevention measures and of a specific surveillance programme.

Which decreases in air pollution should be targeted to bring health and economic benefits and improve environmental justice?

BACKGROUND

Fine particulate matter (PM_2.5) exposure entails large health effects in many urban areas. Public measures aiming at decreasing air pollution are often designed without targeting an explicit health benefit. Our objective was to investigate the health and economic benefits and the social inequalities in exposure resulting from several scenarios of reduction of PM_2.5 exposure, in order to support decisions about urban policies.

MATERIAL AND METHODS

In the French conurbations of Grenoble and Lyon (0.4 and 1.4 million inhabitants, respectively), PM_2.5 yearly average exposure was estimated on a 10-m grid by coupling a PM_2.5 dispersion model to population density. Changes in death cases, life expectancy, lung cancer and term low birth weight incident cases as well as associated health economic costs were estimated for ten PM_2.5 reduction scenarios differing in terms of amplitude of reduction and spatial extent. Changes in social differences in PM_2.5 exposure were also assessed.

RESULTS

During the 2015-2017 period, PM_2.5 average exposure was 13.9 μg/m³ in Grenoble and 15.3 μg/m³ in Lyon conurbations. Exposure to PM_2.5 led to an estimated 145 (95% Confidence Interval, CI, 90-199) and 531 (95% CI, 330-729) premature deaths, 16 (95% CI, 8-24) and 65 (95% CI, 30-96) incident lung cancers, and 49 (95% CI, 19-76) and 193 (95% CI, 76-295) term low birth weight cases each year in Grenoble and Lyon conurbations, respectively, compared to a situation without PM_2.5 anthropogenic sources, i.e. a PM_2.5 concentration of 4.9 μg/m³. The associated costs amounted to 495 (Grenoble) and 1767 (Lyon) M€/year for the intangible costs related to all-cause non-accidental mortality and 27 and 105 M€ for the tangible and intangible costs induced by lung cancer. A PM_2.5 exposure reduction down to the WHO air quality guideline (10 μg/m³) would reduce anthropogenic PM_2.5-attributable mortality by half while decreases by 2.9 μg/m³ (Grenoble) and 3.3 μg/m³ (Lyon) were required to reduce it by a third. Scenarios focusing only on the most exposed areas had little overall impact. Scenarios seeking to reach a homogeneous exposure in the whole study area were the most efficient in alleviating social inequalities in exposure.

CONCLUSIONS

Reduction scenarios targeting only air pollution hotspots had little expected impact on population health. We provided estimates of the PM_2.5 change required to reduce PM_2.5-attributable mortality by one third or more. Our approach can help targeting air pollution reduction scenarios expected to entail significant benefits, and it could easily be transposed to other urban areas.

The impact of ambient air pollution on hospital admissions

Ambient air pollution is the environmental factor with the most significant impact on human health. Several epidemiological studies provide evidence for an association between ambient air pollution and human health. However, the recent economic literature has challenged the identification strategy used in these studies. This paper contributes to the ongoing discussion by investigating the association between ambient air pollution and morbidity using hospital admission data from Switzerland. Our identification strategy rests on the construction of geographically explicit pollution measures derived from a dispersion model that replicates atmospheric conditions and accounts for several emission sources. The reduced form estimates account for location and time fixed effects and show that ambient air pollution has a substantial impact on hospital admissions. In particular, we show that [Formula: see text] and [Formula: see text] are positively associated with admission rates for coronary artery and cerebrovascular diseases while we find no similar correlation for PM10 and [Formula: see text]. Our robustness checks support these findings and suggest that dispersion models can help in reducing the measurement error inherent to pollution exposure measures based on station-level pollution data. Therefore, our results may contribute to a more accurate evaluation of future environmental policies aiming at a reduction of ambient air pollution exposure.

Comparisons of models with water, sediment and biota data

A number of marine radionuclide dispersion models (both Eulerian and Lagrangian) were applied to simulate ¹³⁷Cs releases from Fukushima Daiichi nuclear power plant accident in 2011 over the Pacific at oceanic scale. Simulations extended over two years and both direct releases into the ocean and deposition of atmospheric releases on the ocean surface were considered. Dispersion models included an embedded biological uptake model (BUM). Three types of BUMs were used: equilibrium, dynamic and allometric. Model results were compared with ¹³⁷Cs measurements in water (surface, intermediate and deep layers), sediment and biota (zooplankton, non-piscivorous and piscivorous fish). A reasonable agreement in model/model and model/data comparisons was obtained.

Dispersion pattern of 226Ra and 235U using the ERICA Tool in the coastal mining area, Ierissos Gulf, Greece

Natural radionuclides, present in mining materials, can exhibit elevated values, thus it is of great interest to study their dispersion in mining areas. Radionuclide spatial variations were determined in coastal surface sediments near the mining area of Ierissos Gulf in northern Greece. ²²⁶Ra and ²³⁵U measured concentrations were compared with the estimations of ERICA Tool, the dispersion patterns were derived and the affected region around the load-out pier area was calculated to be approximately 21 km².

Improved impact assessment of odorous compounds from landfills using Monte Carlo simulation

Landfills are city infrastructures used for the treatment of municipal solid waste (MSW) in China. However, due to technical failure and/or management problem most of them are facing serious secondary pollution such as groundwater contamination and odor nuisance. The latter is the main reason causing a growing number of public complaints. Atmospheric dispersion models are routinely adopted for odor impact assessment, but these models provide deterministic predictions only. To determine the potential odorant paths and treat the uncertainty of odor pollution, Monte Carlo simulation coupled with an odor dispersion model was proposed and named Monte Carlo-dispersion simulation method (MCDSM). By introducing a series of random values of error components in the dispersion model, MCDSM can produce probabilistic odor impact results. Values of these variances were randomly selected according to their probability density functions (PDFs) due to the imprecise knowledge of the meteorological and emission conditions. After running the odor dispersion model for numerous times, the randomization produces a set of possible results that closely resembles the expected behavior of the odorants. This study applied MCDSM to estimate the odor impact of methyl mercaptan (CH₃SH) on an MSW landfill in Beijing, China. The PDF of the CH₃SH emission rate was derived from the field data. The uncertainty of odor impact was analyzed statistically, and the results were summarized using the probability of odor exceedance (POE). A POE map of CH₃SH was plotted for a particular interest, in which the north downwind direction was the most polluted area. MCDSM provides a scientific approach for the assessment of odor pollution from individual odorant, which can benefit the formulation of standard for odor impact assessment in landfill sites.

Organophosphorus esters (OPEs) in PM2.5 in urban and e-waste recycling regions in southern China: concentrations, sources, and emissions

Organophosphate esters (OPEs) are novel ubiquitous contaminants that are attracting growing concern, but their emissions into the environment are still poorly understood. In this study, 12 OPEs were measured in fine particulate matter (PM_2.5) at 20 industrial sites in an urban region and four e-waste recycling facilities in a rural region in southern China. There was no significant difference in the concentrations of ∑OPEs between the urban region (519-62,747 pg/m³, median = 2854 pg/m³) and the rural e-waste region (775-13,823 pg/m³, 3321 pg/m³). High OPE concentrations in urban PM_2.5 were generally associated with the electrical, electronic, plastic, and chemical industries. There were no significant correlations between most OPEs in these two regions, suggesting different emission mechanisms. The average emissions of ∑OPEs estimated using a simplified dispersion model were 73.0 kg/yr from the urban industrial point sources and 33.2 kg/yr from the e-waste recycling facilities. The estimated emission inventory from industrial activities in the whole city (3228-4452 kg/yr) was approximately 30-fold higher than that from the e-waste recycling (133 kg/yr) facilities because urban region has a much larger industrial scale. To the best of our knowledge, this is the first effort to model the emissions of OPEs from industrial and e-waste recycling activities to the atmosphere.

Operational evaluation of the RLINE dispersion model for studies of traffic-related air pollutants

Exposure to traffic-related air pollutants (TRAP) remains a key public health issue, and improved exposure measures are needed to support health impact and epidemiologic studies and inform regulatory responses. The recently developed Research LINE source model (RLINE), a Gaussian line source dispersion model, has been used in several epidemiologic studies of TRAP exposure, but evaluations of RLINE's performance in such applications have been limited. This study provides an operational evaluation of RLINE in which predictions of NO_x, CO and PM_2.5 are compared to observations at air quality monitoring stations located near high traffic roads in Detroit, MI. For CO and NO_x, model performance was best at sites close to major roads, during downwind conditions, during weekdays, and during certain seasons. For PM_2.5, the ability to discern local and particularly the traffic-related portion was limited, a result of high background levels, the sparseness of the monitoring network, and large uncertainties for certain processes (e.g., formation of secondary aerosols) and non-mobile sources (e.g., area, fugitive). Overall, RLINE's performance in near-road environments suggests its usefulness for estimating spatially- and temporally-resolved exposures. The study highlights considerations relevant to health impact and epidemiologic applications, including the importance of selecting appropriate pollutants, using appropriate monitoring approaches, considering prevailing wind directions during study design, and accounting for uncertainty.

Nuclear accident consequence assessment in Hong Kong using JRODOS

The JRODOS (Java-based Real-time Online DecisiOn Support) is a decision support system for off-site emergency management for releases of radioactive material into the environment. This paper documents the application of JRODOS by the Hong Kong Observatory in accident consequence assessment and emergency preparedness studies. For operational considerations, the most computational efficient dispersion model in JRODOS, ATSTEP, is adopted. Verification studies for JRODOS's ATSTEP model have been conducted. Comparison with tracer experiment results showed that under neutral atmospheric conditions and distances up to 50 km, the JRODOS simulation outputs were in general of the same order of magnitude with the tracer data. To further evaluate the capability of JRODOS in short-range simulation, a case study on the Fukushima nuclear power plant accident was also carried out. JRODOS was able to produce realistic simulation results which were comparable to the actual airborne monitoring data of the Cs-137 ground deposition from the Fukushima accident. Furthermore, the results of a comprehensive study to assess the potential consequences of accidents at a nearby nuclear power station are presented. Simulation using the French S3 source term for the Guangdong Nuclear Power Station at Daya Bay showed that the projected effective doses within Hong Kong remain far below the IAEA generic criteria of projected dose for urgent protective actions in sheltering/evacuation, while the projected equivalent dose in thyroid may meet the IAEA generic criteria for use of thyroid blocking agent at some areas in the northeastern part of Hong Kong, at distances of up to about 40 km from Daya Bay depending on the prevailing weather conditions in different seasons.

Model-data comparison of high frequency compressional wave attenuation in water-saturated granular medium with bimodal grain size distribution

Several acoustic models, such as the poro-elastic model, visco-elastic model, and multiple scattering model, have been used for describing the dispersion relation in a porous granular medium. However, these models are based on continuum or scattering theory, and therefore cannot explain the broadband measurements in cases where scattering and non-scattering losses co-exist. Additionally, since the models assume that the porous granular medium consists of grains of identical size (unimodal size distribution), the models does not account for the behavior of wave dispersion in a medium that has a distribution of differing grain sizes. As an alternative approach, this study proposes a new broadband attenuation model that describes the high frequency dispersion relation for the p-wave in the case of elastic grain scatterers existing in the background fluid medium. The broadband model combines the Biot-Stoll plus grain contact squirt and shear flow (BICSQS) model and the quasicrystalline approximation (QCA) multiple scattering model. Additionally, distribution of grain size effect is examined rudimentarily through consideration of bimodal grain size distribution. Through the quantitative analysis of the broadband model and measured data, it is shown that the model can explain the attenuation dependencies of frequency and grain size distribution for a water-saturated granular medium in the frequency range from 350kHz to 1.1MHz. This study can be applied to the high frequency acoustic SONAR modeling and design in the water-saturated environment.

Modelling of marine radionuclide dispersion in IAEA MODARIA program: Lessons learnt from the Baltic Sea and Fukushima scenarios

State-of-the art dispersion models were applied to simulate (137)Cs dispersion from Chernobyl nuclear power plant disaster fallout in the Baltic Sea and from Fukushima Daiichi nuclear plant releases in the Pacific Ocean after the 2011 tsunami. Models were of different nature, from box to full three-dimensional models, and included water/sediment interactions. Agreement between models was very good in the Baltic. In the case of Fukushima, results from models could be considered to be in acceptable agreement only after a model harmonization process consisting of using exactly the same forcing (water circulation and parameters) in all models. It was found that the dynamics of the considered system (magnitude and variability of currents) was essential in obtaining a good agreement between models. The difficulties in developing operative models for decision-making support in these dynamic environments were highlighted. Three stages which should be considered after an emergency, each of them requiring specific modelling approaches, have been defined. They are the emergency, the post-emergency and the long-term phases.

Identification and elucidation of anthropogenic source contribution in PM10 pollutant: Insight gain from dispersion and receptor models

Source apportionment study of PM₁₀ (Particulate Matter) in a critically polluted area of Jharia coalfield, India has been carried out using Dispersion model, Principle Component Analysis (PCA) and Chemical Mass Balance (CMB) techniques. Dispersion model Atmospheric Dispersion Model (AERMOD) was introduced to simplify the complexity of sources in Jharia coalfield. PCA and CMB analysis indicates that monitoring stations near the mining area were mainly affected by the emission from open coal mining and its associated activities such as coal transportation, loading and unloading of coal. Mine fire emission also contributed a considerable amount of particulate matters in monitoring stations. Locations in the city area were mostly affected by vehicular, Liquid Petroleum Gas (LPG) & Diesel Generator (DG) set emissions, residential, and commercial activities. The experimental data sampling and their analysis could aid understanding how dispersion based model technique along with receptor model based concept can be strategically used for quantitative analysis of Natural and Anthropogenic sources of PM₁₀.

Contaminant dispersion prediction and source estimation with integrated Gaussian-machine learning network model for point source emission in atmosphere

Gas dispersion model is important for predicting the gas concentrations when contaminant gas leakage occurs. Intelligent network models such as radial basis function (RBF), back propagation (BP) neural network and support vector machine (SVM) model can be used for gas dispersion prediction. However, the prediction results from these network models with too many inputs based on original monitoring parameters are not in good agreement with the experimental data. Then, a new series of machine learning algorithms (MLA) models combined classic Gaussian model with MLA algorithm has been presented. The prediction results from new models are improved greatly. Among these models, Gaussian-SVM model performs best and its computation time is close to that of classic Gaussian dispersion model. Finally, Gaussian-MLA models were applied to identifying the emission source parameters with the particle swarm optimization (PSO) method. The estimation performance of PSO with Gaussian-MLA is better than that with Gaussian, Lagrangian stochastic (LS) dispersion model and network models based on original monitoring parameters. Hence, the new prediction model based on Gaussian-MLA is potentially a good method to predict contaminant gas dispersion as well as a good forward model in emission source parameters identification problem.

Air pollution, health and social deprivation: A fine-scale risk assessment

Risk assessment studies often ignore within-city variations of air pollutants. Our objective was to quantify the risk associated with fine particulate matter (PM2.5) exposure in 2 urban areas using fine-scale air pollution modeling and to characterize how this risk varied according to social deprivation. In Grenoble and Lyon areas (0.4 and 1.2 million inhabitants, respectively) in 2012, PM2.5 exposure was estimated on a 10×10m grid by coupling a dispersion model to population density. Outcomes were mortality, lung cancer and term low birth weight incidences. Cases attributable to air pollution were estimated overall and stratifying areas according to the European Deprivation Index (EDI), taking 10µg/m(3) yearly average as reference (counterfactual) level. Estimations were repeated assuming spatial homogeneity of air pollutants within urban area. Median PM2.5 levels were 18.1 and 19.6μg/m(3) in Grenoble and Lyon urban areas, respectively, corresponding to 114 (5.1% of total, 95% confidence interval, CI, 3.2-7.0%) and 491 non-accidental deaths (6.0% of total, 95% CI 3.7-8.3%) attributable to long-term exposure to PM2.5, respectively. Attributable term low birth weight cases represented 23.6% of total cases (9.0-37.1%) in Grenoble and 27.6% of cases (10.7-42.6%) in Lyon. In Grenoble, 6.8% of incident lung cancer cases were attributable to air pollution (95% CI 3.1-10.1%). Risk was lower by 8 to 20% when estimating exposure through background stations. Risk was highest in neighborhoods with intermediate to higher social deprivation. Risk assessment studies relying on background stations to estimate air pollution levels may underestimate the attributable risk.

Dose estimation for nuclear power plant 4 accident in Taiwan at Fukushima nuclear meltdown emission level

An advanced Gaussian trajectory dispersion model is used to evaluate the evacuation zone due to a nuclear meltdown at the Nuclear Power Plant 4 (NPP4) in Taiwan, with the same emission level as that occurred at Fukushima nuclear meltdown (FNM) in 2011. Our study demonstrates that a FNM emission level would pollute 9% of the island's land area with annual effective dose ≥50 mSv using the meteorological data on 11 March 2011 in Taiwan. This high dose area is also called permanent evacuation zone (denoted as PEZ). The PEZ as well as the emergency-planning zone (EPZ) are found to be sensitive to meteorological conditions on the event. In a sunny day under the dominated NE wind conditions, the EPZ can be as far as 100 km with the first 7-day dose ≥20 mSv. Three hundred sixty-five daily events using the meteorological data from 11 March 2011 to 9 March 2012 are evaluated. It is found that the mean land area of Taiwan in becoming the PEZ is 11%. Especially, the probabilities of the northern counties/cities (Keelung, New Taipei, Taipei, Taoyuan, Hsinchu City, Hsinchu County and Ilan County) to be PEZs are high, ranging from 15% in Ilan County to 51% in Keelung City. Note that the total population of the above cities/counties is as high as 10 million people. Moreover, the western valleys of the Central Mountain Range are also found to be probable being PEZs, where all of the reservoirs in western Taiwan are located. For example, the probability can be as high as 3% in the far southern-most tip of Taiwan Island in Pingtung County. This shows that the entire populations in western Taiwan can be at risk due to the shortage of clean water sources under an event at FNM emission level, especially during the NE monsoon period.

Parametric retrieval model for estimating aerosol size distribution via the AERONET, LAGOS station

The size characteristics of atmospheric aerosol over the tropical region of Lagos, Southern Nigeria were investigated using two years of continuous spectral aerosol optical depth measurements via the AERONET station for four major bands i.e. blue, green, red and infrared. Lagos lies within the latitude of 6.465°N and longitude of 3.406°E. Few systems of dispersion model was derived upon specified conditions to solve challenges on aerosols size distribution within the Stokes regime. The dispersion model was adopted to derive an aerosol size distribution (ASD) model which is in perfect agreement with existing model. The parametric nature of the formulated ASD model shows the independence of each band to determine the ASD over an area. The turbulence flow of particulates over the area was analyzed using the unified number (Un). A comparative study via the aid of the Davis automatic weather station was carried out on the Reynolds number, Knudsen number and the Unified number. The Reynolds and Unified number were more accurate to describe the atmospheric fields of the location. The aerosols loading trend in January to March (JFM) and August to October (ASO) shows a yearly 15% retention of aerosols in the atmosphere. The effect of the yearly aerosol retention can be seen to partly influence the aerosol loadings between October and February.

The relation between modeled odor exposure from livestock farming and odor annoyance among neighboring residents

PURPOSE

Odor annoyance is an important environmental stressor for neighboring residents of livestock farms and may affect their quality of life and health. However, little is known about the relation between odor exposure due to livestock farming and odor annoyance. Even more, the relation between odor exposure and odor annoyance is rather complicated due to variable responses among individuals to comparable exposure levels and a large number of factors (such as age, gender, education) that may affect the relation. In this study, we (1) investigated the relation between modeled odor exposure and odor annoyance; (2) investigated whether other factors can affect this relation; and (3) compared our dose-response relation to a dose-response relation established in a previous study carried out in the Netherlands, more than 10 years ago, in order to investigate changes in odor perception and appreciation over time.

METHODS

We used data from 582 respondents who participated in a questionnaire survey among neighboring residents of livestock farms in the south of the Netherlands. Odor annoyance was established by two close-ended questions in a questionnaire; odor exposure was estimated using the Stacks dispersion model.

RESULTS

The results of our study indicate a statistically significant and positive relation between modeled odor exposure and reported odor annoyance from livestock farming (OR 1.92; 95 % CI 1.53-2.41). Furthermore, age, asthma, education and perceived air pollution in the environment are all related to odor annoyance, although they hardly affect the relation between estimated livestock odor exposure and reported odor annoyance. We also found relatively more odor annoyance reported among neighboring residents than in a previous study conducted in the Netherlands.

CONCLUSIONS

We found a strong relation between modeled odor exposure and odor annoyance. However, due to some uncertainties and small number of studies on this topic, further research and replication of results is recommended.

Monitoring of pile composting process of OFMSW at full scale and evaluation of odour emission impact

In this study, the evolution of odour concentration (ouE/m(3)STP) emitted during the pile composting of the organic fraction of municipal solid waste (OFMSW) was monitored by dynamic olfactometry. Physical-chemical variables as well as the respirometric variables were also analysed. The aim of this work was twofold. The first was to determine the relationship between odour and traditional variables to determine if dynamic olfactometry is a feasible and adequate technique for monitoring an aerobic stabilisation process (composting). Second, the composting process odour impact on surrounding areas was simulated by a dispersion model. The results showed that the decrease of odour concentration, total organic carbon and respirometric variables was similar (around 96, 96 y 98% respectively). The highest odour emission (5224 ouE/m(3)) was reached in parallel with the highest microbiological activity (SOUR and OD20 values of 25 mgO2/gVS · h and 70 mgO2/gVS, respectively). The validity of monitoring odour emissions during composting in combination with traditional and respirometric variables was demonstrated by the adequate correlation obtained between the variables. Moreover, the quantification of odour emissions by dynamic olfactometry and the subsequent application of the dispersion model permitted making an initial prediction of the impact of odorous emissions on the population. Finally, the determination of CO2 and CH4 emissions allowed the influence of composting process on carbon reservoirs and global warming to be evaluated.

Mortality and morbidity in a population exposed to multiple sources of air pollution: A retrospective cohort study using air dispersion models

BACKGROUND AND AIMS

A landfill, an incinerator, and a refinery plant have been operating since the early 1960s in a contaminated site located in the suburb of Rome (Italy). To evaluate their potential health effects, a population-based retrospective cohort study was conducted using dispersion modeling for exposure assessment.

METHODS

A fixed cohort was enrolled in the Rome Longitudinal Study in 2001, mortality and hospitalizations were followed-up until 2010. Exposure assessments to the landfill (H2S), the incinerator (PM10), and the refinery plant (SOX) were performed for each subject using a Lagrangian dispersion model. Individual and small-area variables were available (including exposures levels to NO2 from traffic and diesel trucks). Cox regression analysis was performed (hazard ratios, HRs, 95% CI) using linear terms for the exposures (5th-95th percentiles difference). Single and bi-pollutant models were run.

RESULTS

The cohort included 85,559 individuals. The estimated annual average exposures levels were correlated. H2S from the landfill was associated with cardiovascular hospital admissions in both genders (HR 1.04 95% CI 1.00-1.09 in women); PM10 from the incinerator was associated with pancreatic cancer mortality in both genders (HR 1.40 95% CI 1.03-1.90 in men, HR 1.47 95% CI 1.12-1.93 in women) and with breast morbidity in women (HR 1.13 95% CI 1.00-1.27). SOx from the refinery was associated with laryngeal cancer mortality in women (HR 4.99 95% CI 1.64-15.9) and respiratory hospital admissions (HR 1.13 95% CI 1.01-1.27).

CONCLUSIONS

We found an association of the pollution sources with some cancer forms and cardio-respiratory diseases. Although there was a high correlation between the estimated exposures, an indication of specific effects from the different sources emerged.

Modeling particle number concentrations along Interstate 10 in El Paso, Texas

Annual average daily particle number concentrations around a highway were estimated with an atmospheric dispersion model and a land use regression model. The dispersion model was used to estimate particle concentrations along Interstate 10 at 98 locations within El Paso, Texas. This model employed annual averaged wind speed and annual average daily traffic counts as inputs. A land use regression model with vehicle kilometers traveled as the predictor variable was used to estimate local background concentrations away from the highway to adjust the near-highway concentration estimates. Estimated particle number concentrations ranged between 9.8 × 103 particles/cc and 1.3 × 105 particles/cc, and averaged 2.5 × 104 particles/cc (SE 421.0). Estimates were compared against values measured at seven sites located along I10 throughout the region. The average fractional error was 6% and ranged between -1% and -13% across sites. The largest bias of -13% was observed at a semi-rural site where traffic was lowest. The average bias amongst urban sites was 5%. The accuracy of the estimates depended primarily on the emission factor and the adjustment to local background conditions. An emission factor of 1.63 × 1014 particles/veh-km was based on a value proposed in the literature and adjusted with local measurements. The integration of the two modeling techniques ensured that the particle number concentrations estimates captured the impact of traffic along both the highway and arterial roadways. The performance and economical aspects of the two modeling techniques used in this study shows that producing particle concentration surfaces along major roadways would be feasible in urban regions where traffic and meteorological data are readily available.