Long-term Average Concentrations Research Articles

Coherent long-term average indoor radon concentration estimates obtained by electronic and solid state nuclear track detectors

Despite the rapid development of consumer-grade electronic radon monitors, their capabilities to assess long-term average radon concentrations are not systematically investigated. We present the results of a year-long measurement campaign in 32 dwellings and workplaces in two areas with typical and high radon concentrations in Bulgaria. A systematic comparison was made between electronic monitors and the gold standard — solid-state nuclear track detectors (SSNTDs). RadonEye Plus2 (RE) monitors were used, calibrated and metrologically tested at the three participating laboratories. Two SSNTD-based detectors were utilized: passive radon detectors (PRDs) developed by UKHSA, and CDs/DVDs, developed at SU. Two PRDs for quarterly radon measurements, a RE, a CD and a DVD were placed at each location. Additional old CDs/DVDs were collected for retrospective radon estimates. The results for the annual average radon concentrations, estimated by the four methods are presented. The average difference between the REs and PRDs estimate was 8% (median 4%), between the REs and CDs was 5% (median 0.4%) and between REs and DVDs was −10% (median −12%). The Z-score, representing the difference normalized to its uncertainty, in most cases fell within −1 and 1, indicating excellent agreement. The retrospective estimates also demonstrated excellent agreement with the other data. However, in some cases, small but statistically significant biases were identified between REs and SSNTDs or between different SSNTDs, requiring an inclusion of a reference instrument in future studies. Overall, the results imply that with sound metrological assurance, RadonEye Plus2 monitors can assess long-term average radon concentrations with sufficient accuracy.

Using dispersion models at microscale to assess long-term air pollution in urban hot spots: A FAIRMODE joint intercomparison exercise for a case study in Antwerp

In the framework of the Forum for Air Quality Modelling in Europe (FAIRMODE), a modelling intercomparison exercise for computing NO2 long-term average concentrations in urban districts with a very high spatial resolution was carried out. This exercise was undertaken for a district of Antwerp (Belgium). Air quality data includes data recorded in air quality monitoring stations and 73 passive samplers deployed during one-month period in 2016. The modelling domain was 800 × 800 m2. Nine modelling teams participated in this exercise providing results from fifteen different modelling applications based on different kinds of model approaches (CFD – Computational Fluid Dynamics-, Lagrangian, Gaussian, and Artificial Intelligence). Some approaches consisted of models running the complete one-month period on an hourly basis, but most others used a scenario approach, which relies on simulations of scenarios representative of wind conditions combined with post-processing to retrieve a one-month average of NO2 concentrations.The objective of this study is to evaluate what type of modelling system is better suited to get a good estimate of long-term averages in complex urban districts. This is very important for air quality assessment under the European ambient air quality directives. The time evolution of NO2 hourly concentrations during a day of relative high pollution was rather well estimated by all models. Relative to high resolution spatial distribution of one-month NO2 averaged concentrations, Gaussian models were not able to give detailed information, unless they include building data and street-canyon parameterizations. The models that account for complex urban geometries (i.e. CFD, Lagrangian, and AI models) appear to provide better estimates of the spatial distribution of one-month NO2 averages concentrations in the urban canopy. Approaches based on steady CFD-RANS (Reynolds Averaged Navier Stokes) model simulations of meteorological scenarios seem to provide good results with similar quality to those obtained with an unsteady one-month period CFD-RANS simulations.

LUR modeling of long-term average hourly concentrations of NO2 using hyperlocal mobile monitoring data

Mobile monitoring campaigns have effectively captured spatial hyperlocal variations in long-term average concentrations of regulated and unregulated air pollutants. However, their application in estimating spatiotemporally varying maps has rarely been investigated. Tackling this gap, we investigated whether mobile measurements can assess long-term average nitrogen dioxide (NO2) concentrations for each hour of the day. Using mobile NO2 data monitored for 10 months in Amsterdam, we examined the performance of two spatiotemporal land use regression (LUR) methods, Spatiotemporal-Kriging and GTWR (Geographical and Temporal Weighted Regression), alongside two classical spatial LUR models developed separately for each hour. We found that mobile measurements follow the general pattern of fixed-site measurements, but with considerable deviations (indicating collection uncertainty). Leveraging heterogeneous spatiotemporal autocorrelations, GTWR smoothed these deviations and achieved an overall performance of an R2 of 0.49 and a Mean Absolute Error of 6.33 μg/m3, validated by long-term fixed-site measurements (out-of-sample). The other models tested were more affected by the collection uncertainty. We highlighted that the spatiotemporal variations captured in mobile measurements can be used to reconstruct long-term average hourly air pollution maps. These maps facilitate dynamic exposure assessments considering spatiotemporal human activity patterns.

Syntrichia ruralis as a suitable bioindicator for urban areas – the case study of Tallinn city

Environmental pollution is one of the most important problems in urban environment. Mosses are good indicatorsof air pollution. In Estonia, heavy metals have been measured from Pleurozium schreberi and Hylocomium splendens, whichdo not grow in areas of Tallinn with a higher pollution load. In the present study, Cu, Fe and Cd were measured from fivemoss species growing in contaminated as well less polluted areas of Tallinn. Based on stationary and street pollution sourceinventory and air pollution dispersion modelling, the long-term average concentrations of fine particles (PM10) and nitrogenoxides (NOx) in air were estimated. The work revealed that it is possible to find a moss species that is common in Tallinnand grows in both polluted and less polluted areas – Syntrichia ruralis, which is the most suitable species for bioindicationbased on this work. Moss species Ceratodon purpureus accumulated the most Cd, Cu, and Fe, then Brachytheciumrutabulum/Sciuro-hypnum curtum, and Rhytidiadelphus squarrosus the least. Statistically significant higher Fe concentrationswere in the Syntrichia ruralis, compared to the Sciuro-hypnum curtum and Rhytidiadelphus squarrosus. The Syntrichia ruralisalso had significantly higher Cd content compared to the Brachythecium rutabulum/Sciuro-hypnum curtum. The results ofthe GLM analysis showed that the content of various heavy metals depends on the moss species and the degree of fine particlesin the environment, and it didn't depend on whether the moss grows on the soil or a hard substrate such as concrete, stoneor asphalt.

A novel low sampling rate and cost-efficient active sampler for medium/long-term monitoring of gaseous pollutants

Active sampling is a dependable approach for gaseous pollutants monitoring, offering high accuracy and precision that is unaffected by environmental factors such as wind and temperature in comparison to passive sampling. To measure long-term average concentrations while minimizing the use of materials, a reduced sampling rate is necessary. Thus, this study aims to develop a novel low sampling rate (down to 1 mL/min) and cost-efficient active sampler (LASP) for medium/long-term monitoring of gaseous pollutants. The LASP mainly consisted of a syringe pump, a Y-shaped fitting with two one-way valves, and a control unit for intermittent operation. Results showed that LASP can obtain a sampling rate of less than 1 mL/min and sampling rate exhibited a high level of stability. Daily average concentrations measurements for nitrogen dioxide and formaldehyde by LASP had normalized mean biases of 2.8% and 5.2%, respectively. These numbers were − 5.8% and 6.1% for weekly-average samplings. This study demonstrated applications of LASP in real outdoor (daily-average) and indoor (weekly-average) air quality measurements. It worked well with low noise levels, and without interfering with occupants' daily activities. LASP can assist in improving our ability to monitor air quality and pollutants emissions, thereby supporting health research and policy development. Environmental implicationGaseous air pollution is an important hazardous factor threatening human health. Medium/long-term air quality monitoring is essential for outdoor and indoor air quality assessment and control. However, air sampler for medium/long-term sampling is lacking. This study developed a novel low sampling rate and cost-efficient active sampler and applied it to medium/long-term air sampling. The sampler can work at a sampling rate of less than 1 mL/min. This technology provides a feasible strategy for medium/long-term monitoring of gaseous air pollutants in both environments and emission hotspots.

Within-city spatial variations in long-term average outdoor oxidant gas concentrations and cardiovascular mortality: Effect modification by oxidative potential in the Canadian Census Health and Environment Cohort.

We conducted a retrospective cohort study of participants in the Canadian Census Health and Environment Cohort who lived in Toronto or Montreal, Canada, from 2002 to 2015. Cox proportional hazards models were used to estimate associations between outdoor concentrations of oxidant gases (Ox, a redox-weighted average of nitrogen dioxide and ozone) and cardiovascular deaths. Analyses were performed across strata of two measures of PM2.5 oxidative potential and reactive oxygen species concentrations (ROS) adjusting for relevant confounding factors. PM2.5 mass concentration showed little within-city variability, but PM2.5 oxidative potential and ROS were more variable. Spatial variations in outdoor Ox were associated with an increased risk of cardiovascular mortality [HR per 5 ppb = 1.028, 95% confidence interval (CI): 1.001, 1.055]. The effect of Ox on cardiovascular mortality was stronger above the median of each measure of PM2.5 oxidative potential and ROS (e.g., above the median of glutathione-based oxidative potential: HR = 1.045, 95% CI: 1.009, 1.081; below median: HR = 1.000, 95% CI: 0.960, 1.043). Within-city spatial variations in PM2.5 oxidative potential may modify long-term cardiovascular health impacts of Ox. Regions with elevated Ox and PM2.5 oxidative potential may be priority areas for interventions to decrease the population health impacts of outdoor air pollution.

58 Setting Targets for Particles in Outdoor Air: Advice from the Committee on the Medical Effects of Air Pollutants (Comeap)

Abstract The Environment Act (2021) requires the government to set at least one long-term air-quality target, as well as a target for fine particulate matter (PM2.5) air pollution. COMEAP provided independent expert advice to the Department for Environment, Food and Rural Affairs (Defra) to inform the development of targets to reduce both the annual average (mean) concentration of PM2.5 in ambient air and long term population exposures. COMEAP’s advice includes that: a focus on reducing long-term average concentrations of PM2.5 is appropriate evidence indicates that PM2.5 pollution can have harmful effects on people’s health at low concentrations reducing concentrations to, or below, the World Health Organization’s new Air Quality Guideline (5 µg/m3) would benefit public health available studies have not indicated a threshold of effect below which there is no harm reducing exposure of the whole population would achieve the greatest overall public health benefit some individuals or groups are more at risk, but it might be difficult to reflect this in a national targets framework the health benefits of reducing other pollutants, such as nitrogen dioxide and ozone, should not be overlooked. In response to Defra’s evidence report explaining the rationale for its proposed targets, COMEAP: recommended that Defra should consider roles in continuing to improve air quality, even where legally binding targets are met suggested that interim targets would be important to ensure early progress and maximise health benefits stressed the importance of considering inequalities noted that international engagement will be required

Association between smoking and hypertension under different PM2.5 and green space exposure: A nationwide cross-sectional study.

Smoking has been widely reported to have a significant relationship with hypertension, but the past description of this relationship has not been uniform. In addition, there has been a lack of research to discuss the impact of environmental exposure on the relationship between smoking and hypertension. Therefore, this study estimates the association between smoking and hypertension in middle aged and elderly people in China under different PM2.5 (fine particulate matter) concentrations and the green space exposure conditions. Individual sample data from the China Health and Retirement Longitudinal Study in 2018 and the long-term average exposure concentration of fine particles and green space exposure for all participants were used with a multilevel binary logistic mixed effects model. Adjustments were made for sociodemographic characteristics and other health behaviors including drinking, physical activity, and social activity. The normalized difference vegetation index (NDVI) and PM2.5 concentration stratification were assigned with the median of the population exposure concentration as the dividing line, and the dual environmental factor stratification was assigned in combination with the two types of environmental exposure. The analysis was also stratified using age groups. A total of 10,600 participants over the age of 45 were included in the study. The effects of smoking on hypertension were diverse under different environmental exposure conditions. There was a significant relationship between smoking behavior and hypertension in the Low-NDVI group, and the effect value of this relationship was significantly different from that in the High-NDVI group. Furthermore, for respondents exposed to low green spaces and high PM2.5 environments at the same time (Low-NDVI/High-PM2.5 group), their smoking behavior may lead to an increase in the risk of hypertension. In addition, the risk of hypertension caused by smoking in the middle-aged (45-64) was significant under low green space exposure, but the effect difference between the different age groups was not significant. The relationship between smoking and hypertension was different under different environmental exposure conditions. Exposure to low green spaces may strengthen the association between smoking and hypertension risk. When participants were exposed to both low green spaces and high PM2.5 concentrations, the risk of hypertension caused by smoking was significantly higher than that of those who were exposed to high green spaces and low PM2.5 concentrations.

Neighborhood-scale ambient NO2 concentrations using TROPOMI NO2 data: Applications for spatially comprehensive exposure assessment

This study estimated long-term average ambient NO2 concentrations using TROPOspheric Monitoring Instrument (TROPOMI) tropospheric NO2 data and land use information at the spatial resolution of 500 m in California for the years 2018–2019. Our satellite-land use regression model demonstrated reasonably high predictive power with cross-validation (CV) R2 = 0.76, mean absolute error (MAE) = 1.95 ppb, and root mean squared error (RMSE) = 2.51 ppb in a comparison between measured and estimated NO2 concentrations. Exploiting the high-resolution NO2 estimates, we further investigated the representativeness of ground NO2 monitors for population exposures and examined the spatial variation of NO2 in relation to parcel-level property data for exposure attributions. The ground NO2 monitors were the most representative of population exposures in Los Angeles and San Diego counties, supported by population-weighted average NO2 concentrations (satellite-derived estimations) similar to arithmetic average NO2 concentrations (ground measurements). On the contrary, the exposure assessment using the ground monitors was the least representative and protective in Humboldt, San Luis Obispo, and Yolo counties with population-weighted average NO2 greater than arithmetic average NO2 by 82.2 % (1.85 ppb), 67.1 % (1.89 ppb), and 58.2 % (2.48 ppb), respectively. In a case study of LA County, we identified comparatively high NO2 concentrations for the property types of food processing facilities and high-density residential complexes (such as high-rise apartments and apartments). This finding provides evidence that these emerging sources may be crucial to mitigate cumulative NO2 exposures and subsequent health risks from a regulatory perspective.

Long-term air pollution exposure and COVID-19 case-severity: An analysis of individual-level data from Switzerland

Several studies are pointing out that exposure to elevated air pollutants could contribute to increased COVID-19 mortality. However, literature on the associations between air pollution exposure and COVID-19 severe morbidity is rather sparse. In addition, the majority of the studies used an ecological study design and were applied in regions with rather high air pollution levels. Here, we study the differential effects of long-term exposure to air pollution on severe morbidity and mortality risks from COVID-19 in various population subgroups in Switzerland, a country known for clean air. We perform individual-level analyses using data covering the first two major waves of COVID-19 between February 2020 and May 2021. High-resolution maps of particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations were produced for the 6 years preceding the pandemic using Bayesian geostatistical models. Air pollution exposure for each patient was measured by the long-term average concentration across the municipality of residence. The models were adjusted for the effects of individual characteristics, socio-economic, health-system, and climatic factors. The variables with an important association to COVID-19 case-severity were identified using Bayesian spatial variable selection. The results have shown that the individual-level characteristics are important factors related to COVID-19 morbidity and mortality in all the models. Long-term exposure to air pollution appears to influence the severity of the disease only when analyzing data during the first wave; this effect is attenuated upon adjustment for health-system related factors during the entire study period. Our findings suggest that the burden of air pollution increased the risks of COVID-19 in Switzerland during the first wave of the pandemic, but not during the second wave, when the national health system was better prepared.

A Knowledge Transfer Approach to Map Long-Term Concentrations of Hyperlocal Air Pollution from Short-Term Mobile Measurements.

Mobile measurements are increasingly used to develop spatially explicit (hyperlocal) air quality maps using land-use regression (LUR) models. The prevailing design of mobile monitoring campaigns results in the collection of short-term, on-road air pollution measurements during daytime on weekdays. We hypothesize that LUR models trained with such mobile measurements are not optimized for estimating long-term average residential air pollution concentrations. To bridge the knowledge gaps in space (on-road versus near-road) and time (short- versus long-term), we propose transfer-learning techniques to adapt LUR models by transferring the mobile knowledge into long-term near-road knowledge in an end-to-end manner. We trained two transfer-learning LUR models by incorporating mobile measurements of nitrogen dioxide (NO2) and ultrafine particles (UFP) collected by Google Street View cars with long-term near-road measurements from regular monitoring networks in Amsterdam. We found that transfer-learning LUR models performed 55.2% better in predicting long-term near-road concentrations than the LUR model trained only with mobile measurements for NO2 and 26.9% for UFP, evaluated by normalized mean absolute errors. This improvement in model accuracy suggests that transfer-learning models provide a solution for narrowing the knowledge gaps and can improve the accuracy of mapping long-term near-road air pollution concentrations using short-term on-road mobile monitoring data.

Mobile monitoring of air pollutants; performance evaluation of a mixed-model framework in relation to number of drive days

Background and Aim. Because of the implicit uncertainty of mobile measurements due to the short-term nature of the measurements, most studies to date have used empirical modeling to stabilize predicted concentrations, thereby losing local spatial information. In a previous paper, we demonstrated that a mixed-model can stabilise the measurements by a land use regression (LUR) model, while allowing street segments to deviate from the LUR prediction based on between-segment variation of the measurements as a random effect. We analysed how many drive-days are needed for the mixed-effect model to improve predictions from LUR models and data-only mapping. Methods. We used black carbon data from the Air View study in Oakland, where every street segment was measured at least 50 times. We selected one drive day per street segment and compared the measurement, the LUR prediction and the mixed-model prediction with the average concentration based on 50 drive days of that street segment. We assumed that driving 50 times on a street segment reflects a robust long-term average concentration. We then sequentially added drive days to the dataset and computed the explained variance (R²) and RMSE. Results. With one drive day on every street segment, the LUR model explained 63% of the variation, with very limited improvement in performance with increasing number of drive-days (65% for 50 drive days). The data-only map predicts less than 30% for one drive-day and more than 90% of the variance after 15 drive days, surpassing the LUR model in explained variance at 4-5 drive days. The mixed model outperformed the data-only and LUR model estimates, with 75% explained variance after 2 drive days and 90% after 12 drive days. Conclusion. The mixed-model improves predictions compared to LUR and data already with two drive days and updates the model when more drives become available.

Exposure to Long-Term Air Pollution and Incidence of Peripheral Arterial Disease in the General Population: A Korean National Population-Based Retrospective Cohort Study.

This study aimed to evaluate the causal relationship between long-term outdoor air pollutants and incidence of peripheral arterial disease (PAD) using the Korean National Health Insurance Service-National Sample Cohort (NHIS-NSC) database. We included 292,091 subjects from the general population who had previously not been diagnosed with PAD by the NHIS-NSC between 2008 and 2014. Hourly air pollutant data (particulate and gaseous) and climate data were collected. Correlation analysis of the collected data confirmed the relationship between air pollution and PAD incidence. For 1,836,965.4 person-years, incident cases of PAD were observed in 5243 subjects (285.4/100,000 person-years). In the Cox proportional hazard analysis, exposure to long-term average concentration of sulfur dioxide (SO2) [hazard ratio (HR), 1.686; (95% confidence interval (CI), 1.108-2.565) for .01ppm] and nitrogen dioxide (NO2) [HR, 1.200; (95% CI, 1.077-1.336) for .01 ppm] significantly increased the risk of PAD occurrence after the adjustment for several variables. This study demonstrated that SO2 and NO2 exposure are independent predictors of PAD.

Estimating the lateral transfer of organic carbon through the European river network using a land surface model

Abstract. Lateral carbon transport from soils to the ocean through rivers has been acknowledged as a key component of the global carbon cycle, but it is still neglected in most global land surface models (LSMs). Fluvial transport of dissolved organic carbon (DOC) and CO2 has been implemented in the ORCHIDEE LSM, while erosion-induced delivery of sediment and particulate organic carbon (POC) from land to river was implemented in another version of the model. Based on these two developments, we take the final step towards the full representation of biospheric carbon transport through the land–river continuum. The newly developed model, called ORCHIDEE-Clateral, simulates the complete lateral transport of water, sediment, POC, DOC, and CO2 from land to sea through the river network, the deposition of sediment and POC in the river channel and floodplains, and the decomposition of POC and DOC in transit. We parameterized and evaluated ORCHIDEE-Clateral using observation data in Europe. The model explains 94 %, 75 %, and 83 % of the spatial variations of observed riverine water discharges, bankfull water flows, and riverine sediment discharges in Europe, respectively. The simulated long-term average total organic carbon concentrations and DOC concentrations in river flows are comparable to the observations in major European rivers, although our model generally overestimates the seasonal variation of riverine organic carbon concentrations. Application of ORCHIDEE-Clateral for Europe reveals that the lateral carbon transfer affects land carbon dynamics in multiple ways, and omission of this process in LSMs may lead to an overestimation of 4.5 % in the simulated annual net terrestrial carbon uptake over Europe. Overall, this study presents a useful tool for simulating large-scale lateral carbon transfer and for predicting the feedbacks between lateral carbon transfer and future climate and land use changes.

Long-Term Exposure to Air Pollution and Incidence of Venous Thromboembolism in the General Population: A Population-Based Retrospective Cohort Study.

To date, the relationship between air pollutants and venous thromboembolism (VTE) has not been well established. Our aim is to investigate the association between ambient air pollutants and the incidence of VTE using the Korean National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS) database. From 2003 to 2015, 338,616 subjects from the general population not previously diagnosed with VTE were included. The long-term average concentration of air pollutants before diagnosis for each subject was calculated. During the study period, there were 3196 incident cases of VTE. After adjusting for age, gender, economic status, body mass index, physical activity, smoking, alcohol consumption, comorbid diseases, and meteorological variables, the risk of VTE was observed to increase significantly with the long-term average concentration of particulate matter < 10 μm in diameter: PM10 (hazard ratio (HR) = 1.064 (95% confidence interval [CI] 1.053–1.074) for 1 μg/m3), SO2 (HR = 1.118 (95% CI 1.079–1.158) 1 ppb), and O3 (HR = 1.039 (95% CI 1.026–1.053) for 1 ppb), respectively. A difference between the date of the health screening and the date of diagnosis of the disease was observed. Long-term exposure to air pollutants including PM10, SO2, and O3 may be an independent risk factor for the development of VTE.

Feature selection for global tropospheric ozone prediction based on the BO-XGBoost-RFE algorithm

Ozone is one of the most important air pollutants, with significant impacts on human health, regional air quality and ecosystems. In this study, we use geographic information and environmental information of the monitoring site of 5577 regions in the world from 2010 to 2014 as feature input to predict the long-term average ozone concentration of the site. A Bayesian optimization-based XGBoost-RFE feature selection model BO-XGBoost-RFE is proposed, and a variety of machine learning algorithms are used to predict ozone concentration based on the optimal feature subset. Since the selection of the underlying model hyperparameters is involved in the recursive feature selection process, different hyperparameter combinations will lead to differences in the feature subsets selected by the model, so that the feature subsets obtained by the model may not be optimal solutions. We combine the Bayesian optimization algorithm to adjust the parameters of recursive feature elimination based on XGBoost to obtain the optimal parameter combination and the optimal feature subset under the parameter combination. Experiments on long-term ozone concentration prediction on a global scale show that the prediction accuracy of the model after Bayesian optimized XGBoost-RFE feature selection is higher than that based on all features and on feature selection with Pearson correlation. Among the four prediction models, random forest obtained the highest prediction accuracy. The XGBoost prediction model achieved the greatest improvement in accuracy.

Poly- and Perfluoroalkyl Substances in Municipal Wastewater Treatment Plants in the United States: Seasonal Patterns and Meta-Analysis of Long-Term Trends and Average Concentrations

This paper presents an up-to-date meta-analysis assessing per- and polyfluoroalkyl substance (PFAS) concentrations at wastewater treatment plants (WWTPs) as well as changes over time. PFAS concentrations were compiled for WWTPs in the United States from peer-reviewed studies, technical reports, and original data. Perfluorooctanoic acid (PFOA) increased by an average of 6.0 ± 1.6 ng/L from the influents to the effluents of WWTPs, but perfluorosulfonic acid (PFOS) did not significantly change, indicating sorption to sludge is offset by biotransformation of precursor compounds. The occurrence of individual PFAS may vary temporally; for example, perfluoropentanoic acid correlated weakly with seasonal temperatures at a site in Virginia. Wastewater effluent PFOA concentrations decreased at a site in Nevada from 2012 to February 2020 but appeared to increase during the COVID-19 pandemic. Effluent PFOA also declined nationally from 1999 to 2020 by ∼13% per year. Nevertheless, the national mean PFOA concentration was 8.4 ± 0.4 ng/L in data collected from 2013 to 2020 with outliers omitted, indicating persisting low-level occurrence. This would equate to 383 ± 20 kg of PFOA per year continuing to enter the environment via WWTP effluents.

Study of the naturally occurring radionuclide Beryllium-7 (Be-7) in Hong Kong

Beryllium-7 (Be-7) is one of the naturally occurring radionuclides being monitored under the Global Atmosphere Watch Programme of the World Meteorological Organization. Be-7 mainly originates from cosmic rays. It can be used as a tracer to facilitate understanding of the atmospheric vertical transport by observing its spatial and temporal distribution characteristics.The Hong Kong Observatory has been operating an environmental radiation monitoring programme for decades, and long record of Be-7 activity concentration data in airborne particulate samples are available to characterize the behaviour of Be-7 in the lower atmosphere in Hong Kong.In this study, Be-7 activity concentration data of airborne particulates collected at three locations in Hong Kong from 1993 to 2020 are examined. Temporal variations are analyzed. In particular, the long-term monthly average Be-7 activity concentrations are found to be most sensitive to precipitation. The relevant data analysis, as well as key factors affecting the Be-7 activity concentrations in the lower atmosphere in Hong Kong, will be described.

Estimating long-term average household air pollution concentrations from repeated short-term measurements in the presence of seasonal trends and crossover.

Estimating long-term exposure to household air pollution is essential for quantifying health effects of chronic exposure and the benefits of intervention strategies. However, typically only a small number of short-term measurements are made. We compare different statistical models for combining these short-term measurements into predictions of a long-term average, with emphasis on the impact of temporal trends in concentrations and crossover in study design. We demonstrate that a linear mixed model that includes time adjustment provides the best predictions of long-term average, which have lower error than using household averages or mixed models without time, for a variety of different study designs and underlying temporal trends. In a case study of a cookstove intervention study in Honduras, we further demonstrate how, in the presence of strong seasonal variation, long-term average predictions from the mixed model approach based on only two or three measurements can have less error than predictions based on an average of up to six measurements. These results have important implications for the efficiency of designs and analyses in studies assessing the chronic health impacts of long-term exposure to household air pollution.

Impacts of air pollution on COVID-19 case fatality rate: a global analysis.

The coronavirus disease 2019 (COVID-19) pandemic is still rapidly spreading globally. To probe high-risk cities and the impacts of air pollution on public health, this study explores the relationship between the long-term average concentration of air pollution and the city-level case fatality rate (CFR) of COVID-19 globally. Then, geographically weighted regression (GWR) is applied to examine the spatial variability of the relationships. Six air pollution factors, including nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), PM2.5 (particles with diameter ≤2.5 μm), PM10 (particles with diameter ≤10 μm), and air quality index (AQI), are positively associated with the city-level COVID-19 CFR. Our results indicate that a 1-unit increase in NO2 (part per billion, PPB), SO2 (PPB), O3 (PPB), PM2.5 (microgram per cubic meter, μg/m3), PM10 (μg/m3), AQI (score), is related to a 1.450%, 1.005%, 0.992%, 0.860%, 0.568%, and 0.776% increase in the city-level COVID-19 CFR, respectively. Additionally, the effects of NO2, O3, PM2.5, AQI, and probability of living with poor AQI on COVID-19 spatially vary in view of the estimation of the GWR. In other words, the adverse impacts of air pollution on health are different among the cities. In summary, long-term exposure to air pollution is negatively related to the COVID-19 health outcome, and the relationship is spatially non-stationary. Our research sheds light on the impacts of slashing air pollution on public health in the COVID-19 pandemic to help governments formulate air pollution policies in light of the local situations.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11356-021-18442-x.