Suchergebnisse

Over the past two decades numerous research projects have investigated an array of characteristics of ultrafine particles (UFP), or more generally, particles of the lower submicrometre size range. There is a reasonably good scientific understanding of the particle concentration levels, modality of size distributions associated with various sources, and their spatial heterogeneity in the urban environments. However, despite the progress made, there has only been very limited progress in understanding the risk to human health posed by UFP, and therefore whether the particles should be controlled. Part of the reason relates to the challenges in conducting epidemiological studies on the impact of UFP, and lack of consistency between the outcomes of some of the studies. In 2018 a group of exposure experts, toxicologist and epidemiologist joint forces to think outside the box, and to use the wealth of scientific knowledge on UFP physico-chemistry, toxicology and epidemiology to develop an approach that will create the basis for protection against the particles. The group has been working on developing a White Paper to inform decision makers on the state of knowledge on UFP and on conducting meta-analysis of data from epidemiologic studies to identify any evidence that can already be used to recommend exposure limits for UFP. The outcome of this work will contribute to the current debates on UFP, and to the work conducted on this topic by national and international bodies, including the World Health Organization or the European Union. The presentation will summarize the progress of this work and the picture that begins to ...

Covering the fundamentals of air-borne particles and settled dust in the indoor environment, this handy reference investigates: relevant definitions and terminology; characteristics; sources; sampling techniques and instrumentation; exposure assessment; monitoring methods. The result is a useful and comprehensive overview for chemists, physicists and biologists, postgraduate students, medical practitioners, occupational health professionals, building owners and managers, building, construction and air-conditioning engineers, architects, environmental lawyers, government and regulatory professi

In environmental monitoring, the ability to obtain high-quality data across space and time is often limited by the cost of purchasing, deploying and maintaining a large collection of equipment, and the employment of personnel to perform these tasks. An ideal design for a monitoring campaign would be dense enough in time to capture short-range variation at each site, long enough in time to examine trends at each site and across all sites, and dense enough in space to allow modelling of the relationship between the means at each of the sites. This paper outlines a methodology for semiparametric spatiotemporal modelling of data that is dense in time but sparse in space, obtained from a split panel design, the most feasible approach to covering space and time with limited equipment. The data are hourly averaged particle number concentration (PNC) and were collected as part of the International Laboratory for Air Quality and Health's Ultrafine Particles from Traffic Emissions and Children's Health (UPTECH) project. The panel design comprises two weeks of continuous measurements taken at each of a number of government primary schools in the Brisbane Metropolitan Area, with each school visited sequentially. The school data are augmented by data from long-term monitoring stations at three locations in Brisbane, Australia. The temporal part of the model explains daily and weekly cycles in PNC at the schools. The temporal variation is modelled hierarchically with a penalised random walk term common to all sites and a similar term accounting for the remaining temporal trend at each site. The modelling of temporal trends requires an acknowledgement that the observations are correlated rather than independent. At each school and long-term monitoring site, peaks in PNC can be attributed to the morning and afternoon rush hour traffic and new particle formation events. The spatial component of the model describes the school-to-school variation in mean PNC at each school and within each school ground. The spatial term in the model is derived from a stochastic partial differential equation and approximates a Gaussian process with a Gaussian Markov Random field. Fitting the model helps describe spatial and temporal variability at a subset of the UPTECH schools and the long-term monitoring sites, which can be used to estimate the exposure of school children to ultrafine particles. Parameter estimates and their uncertainty are computed in a computationally efficient approximate Bayesian inference environment, R-INLA.

BACKGROUND: There is a significant lack of scientific knowledge on population exposure to ultrafine particles (UFP) in China to date. This paper quantifies and characterises school children's personal UFP exposure and exposure intensity against their indoor and outdoor activities during a school day (home, school and commuting) in the city of Heshan within the Pearl River Delta (PRD) region, southern China. METHODS: Time-series of UFP number concentrations and average size were measured over 24 h for 24 children (9-13 years old), using personal monitors over two weeks in April 2016. Time-activity diaries and a questionnaire on the general home environment and potential sources of particles at home were also collected for each participating child. The analysis included concurrently measured size distributions of ambient UFP at a nearby fixed reference site (Heshan Supersite). RESULTS: Hourly average UFP concentrations exhibited three peaks in the morning, midday and evening. Time spent indoors at home was found to have the highest average exposure (1.26 × 104 cm-3 during sleeping) and exposure intensity (2.41). While there is always infiltration of outdoor particles indoors (from nearby traffic and general urban background sources), indoor exposure at home was significantly higher than outdoor exposure. Based on the collected questionnaire data, this was considered to be driven predominantly by adults smoking and the use of mosquito repellent incense during the night. Outdoor activities at school were associated with the lowest average exposure (6.87 × 102 cm-3) and exposure intensity (0.52). CONCLUSION: Despite the small sample size, this study characterised, for the first time, children's personal UFP exposure in a city downwind of major pollution sources of the PRD region in China. Particularly, the results highlighted the impact of smoking at home on children's exposure. While the study could not apportion the specific contributions of second hand-smoking and mosquito coil burning, considering the prevalence of smokers among the parents who smoke at home, smoking is a very significant factor. Exposure to second-hand smoke is avoidable, and these findings point out to the crucial role of government authorities and public health educators in engaging with the community on the role of air quality on health, and the severity of the impact of second-hand smoke on children's health.

Abstract
STOFFENMANAGER® and the Advanced REACH Tool (ART) are recommended tools by the European Chemical Agency for regulatory chemical safety assessment. The models are widely used and accepted within the scientific community. STOFFENMANAGER® alone has more than 37 000 users globally and more than 310 000 risk assessment have been carried out by 2020. Regardless of their widespread use, this is the first study evaluating the theoretical backgrounds of each model. STOFFENMANAGER® and ART are based on a modified multiplicative model where an exposure base level (mg m−3) is replaced with a dimensionless intrinsic emission score and the exposure modifying factors are replaced with multipliers that are mainly based on subjective categories that are selected by using exposure taxonomy. The intrinsic emission is a unit of concentration to the substance emission potential that represents the concentration generated in a standardized task without local ventilation. Further information or scientific justification for this selection is not provided. The multipliers have mainly discrete values given in natural logarithm steps (…, 0.3, 1, 3, …) that are allocated by expert judgements. The multipliers scientific reasoning or link to physical quantities is not reported. The models calculate a subjective exposure score, which is then translated to an exposure level (mg m−3) by using a calibration factor. The calibration factor is assigned by comparing the measured personal exposure levels with the exposure score that is calculated for the respective exposure scenarios. A mixed effect regression model was used to calculate correlation factors for four exposure group [e.g. dusts, vapors, mists (low-volatiles), and solid object/abrasion] by using ~1000 measurements for STOFFENMANAGER® and 3000 measurements for ART. The measurement data for calibration are collected from different exposure groups. For example, for dusts the calibration data were pooled from exposure measurements sampled from pharmacies, bakeries, construction industry, and so on, which violates the empirical model basic principles. The calibration databases are not publicly available and thus their quality or subjective selections cannot be evaluated. STOFFENMANAGER® and ART can be classified as subjective categorization tools providing qualitative values as their outputs. By definition, STOFFENMANAGER® and ART cannot be classified as mechanistic models or empirical models. This modeling algorithm does not reflect the physical concept originally presented for the STOFFENMANAGER® and ART. A literature review showed that the models have been validated only at the 'operational analysis' level that describes the model usability. This review revealed that the accuracy of STOFFENMANAGER® is in the range of 100 000 and for ART 100. Calibration and validation studies have shown that typical log-transformed predicted exposure concentration and measured exposure levels often exhibit weak Pearson's correlations (r is <0.6) for both STOFFENMANAGER® and ART. Based on these limitations and performance departure from regulatory criteria for risk assessment models, it is recommended that STOFFENMANAGER® and ART regulatory acceptance for chemical safety decision making should be explicitly qualified as to their current deficiencies.

China is challenged with the simultaneous goals of improving air quality and mitigating climate change. The "Beautiful China" strategy, launched by the Chinese government in 2020, requires that all cities in China attain 35 μg/m(3) or below for annual mean concentration of PM(2.5) (particulate matter with aerodynamic diameter less than 2.5 μm) by 2035. Meanwhile, China adopts a portfolio of low-carbon policies to meet its Nationally Determined Contribution (NDC) pledged in the Paris Agreement. Previous studies demonstrated the cobenefits to air pollution reduction from implementing low-carbon energy policies. Pathways for China to achieve dual targets of both air quality and CO(2) mitigation, however, have not been comprehensively explored. Here, we couple an integrated assessment model and an air quality model to evaluate air quality in China through 2035 under the NDC scenario and an alternative scenario (Co-Benefit Energy [CBE]) with enhanced low-carbon policies. Results indicate that some Chinese cities cannot meet the PM(2.5) target under the NDC scenario by 2035, even with the strictest end-of-pipe controls. Achieving the air quality target would require further reduction in emissions of multiple air pollutants by 6 to 32%, driving additional 22% reduction in CO(2) emissions relative to the NDC scenario. Results show that the incremental health benefit from improved air quality of CBE exceeds 8 times the additional costs of CO(2) mitigation, attributed particularly to the cost-effective reduction in household PM(2.5) exposure. The additional low-carbon energy polices required for China's air quality targets would lay an important foundation for its deep decarbonization aligned with the 2 °C global temperature target.

The real-time monitoring of reductions of economic activity by containment measures and its effect on the transmission of the coronavirus (COVID-19) is a critical unanswered question. We inferred 5,642 weekly activity anomalies from the meteorology-adjusted differences in spaceborne tropospheric NO(2) column concentrations after the 2020 COVID-19 outbreak relative to the baseline from 2016 to 2019. Two satellite observations reveal reincreasing economic activity associated with lifting control measures that comes together with accelerating COVID-19 cases before the winter of 2020/2021. Application of the near-real-time satellite NO(2) observations produces a much better prediction of the deceleration of COVID-19 cases than applying the Oxford Government Response Tracker, the Public Health and Social Measures, or human mobility data as alternative predictors. A convergent cross-mapping suggests that economic activity reduction inferred from NO(2) is a driver of case deceleration in most of the territories. This effect, however, is not linear, while further activity reductions were associated with weaker deceleration. Over the winter of 2020/2021, nearly 1 million daily COVID-19 cases could have been avoided by optimizing the timing and strength of activity reduction relative to a scenario based on the real distribution. Our study shows how satellite observations can provide surrogate data for activity reduction during the COVID-19 pandemic and monitor the effectiveness of containment to the pandemic before vaccines become widely available.