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Statistical modelling of spatial and temporal variation in urban particle number size distribution at traffic and background sites

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ATMOSPHERIC ENVIRONMENT 2021年 244卷 117925-117925页

作者： Gerling, Lars Wiedensohler, Alfred Weber, Stephan Tech Univ Carolo Wilhelmina Braunschweig Inst Geoecol Climatol & Environm Meteorol Langer Kamp 19c D-38106 Braunschweig Germany Leibniz Inst Tropospher Res TROPOS Permoserstr 15 D-04318 Leipzig Germany

Ultrafine particles (UFP) pose a risk to human health, but due to the multitude of sources and fast transformation in the urban atmosphere, quantifying the exposure is challenging. Furthermore, physical properties of aerosol particles depend on the particle size. Statistical models are used to quantify spatial and temporal variation of UFP, but rarely used for particle number size distribution (PNSD). The aim of the study was to establish an interpretable statistical model capturing spatial and temporal variation of urban PNSDs using generalized additive models (GAM) and multivariate adaptive regression spline models (MARS). These algorithms automatically fit interpretable, non-linear marginal function to represent relationships between explanatory and response variables. Three different approaches were evaluated to cope with the multidimensionality of the PNSD data (20-800 nm, 34 size bins): a generalized additive model for the particle number concentration (PNC) of every individual size bin (GAM(bins)), a generalized additive model for the parameters of the PNSD function (GAM(pams)) and a multivariate adaptive regression spline model for the PNC of every size bin (MARS(bins)). Reanalysis data of meteorological quantities, urban geometry parameters and approximated traffic counts were used as explanatory variables. Marginal functions of the final models could be attributed to major processes that contribute to spatial and temporal variation of the PNSD, i.e. emissions from vehicle traffic, transport, dilution, accumulation, deposition and new particle formation. Cross-validation coefficients of determination ranged between 0.27 and 0.48 for most size bins. Nonetheless, the modelling approaches resulted in similar root mean square errors (RMSE) and mean absolute error (MAE). Though direct spatial transferability of the models is limited, the presented approaches may be useful for estimating ambient exposure to particles.

关键词： Urban air quality Ultrafine particles Particle number size distribution generalized additive model Multivariate adaptive regression spline Statistical model

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Spatial and temporal patterns in catch rates from multispecies shark fisheries in Western Australia

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OCEAN & COASTAL MANAGEMENT 2021年 213卷 105883-105883页

作者： Braccini, Matias Denham, Ainslie O'Neill, Michael F. Lai, Eva Dept Primary Ind & Reg Dev Western Australia Western Australian Fisheries & Marine Res Labs POB 20 North Beach WA 6920 Australia Agrisci Queensland Maroochy Res Facil Dept Agr & Fisheries POB 5083 SCMC Nambour Qld 4560 Australia

Time series of commercial catch rates are the most commonly available proxy for shark abundance trends. Biased interpretations of spatial and temporal patterns in commercial catch rates have led to misleading stock status assessments. In this study, we outlined the steps required to account for unbalanced fishery data sets and derive standardised catch rates for the main commercial species taken in the shark fisheries of Western Australia (WA) as a case study. Catch rates peaked early in the year for sandbar (March) and dusky (March-April) sharks but towards the end of the year (September-November) for whiskery sharks. For gummy sharks, catch rates fluctuated throughout the year. Catch rates increased with depth for whiskery and sandbar sharks but decreased with depth for dusky sharks. Spatial hotspots occurred in the southwest and east of 123 degrees E for whiskery sharks, east of 124 degrees for gummy sharks, in the southwest for dusky sharks, and in the west coast between 28 degrees and 30 degrees S for sandbar sharks. Following an initial decline, annual catch rates stabilized in recent years across all species. Our study illustrated the challenges associated with multispecies fisheries data for informing management and conservation, identified spatial hotspots and provided standardised catch rates for use in stock assessments. Given the lower economic value of sharks, fishery-independent surveying is unlikely to be achievable for most species. Hence, applying best practices for standardising available shark fishery-dependent data and communicating the corresponding uncertainty is essential for quantitative assessments.

关键词： Australia Elasmobranchs generalized additive model Non-linear relation

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Estimating monthly PM2.5 concentrations from satellite remote sensing data, meteorological variables, and land use data using ensemble statistical modeling and a random forest approach

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ENVIRONMENTAL POLLUTION 2021年 291卷 118159-118159页

作者： Chen, Chu-Chih Wang, Yin-Ru Yeh, Hung-Yi Lin, Tang-Huang Huang, Chun-Sheng Wu, Chang-Fu Natl Hlth Res Inst Inst Populat Hlth Sci Div Biostat & Bioinformat Zhunan Township Taiwan Kaohsiung Med Univ Res Ctr Environm Med Kaohsiung Taiwan Natl Cent Univ Ctr Space & Remote Sensing Res Taoyuan Taiwan Natl Taiwan Univ Sch Publ Hlth Inst Environm & Occupat Hlth Sci Taipei Taiwan

Fine particulate matter (PM2.5) is associated with various adverse health outcomes and poses serious concerns for public health. However, ground monitoring stations for PM2.5 measurements are mostly installed in populationdense or urban areas. Thus, satellite retrieved aerosol optical depth (AOD) data, which provide spatial and temporal surrogates of exposure, have become an important tool for PM2.5 estimates in a study area. In this study, we used AOD estimates of surface PM2.5 together with meteorological and land use variables to estimate monthly PM2.5 concentrations at a spatial resolution of 3 km2 over Taiwan Island from 2015 to 2019. An ensemble twostage estimation procedure was proposed, with a generalized additive model (GAM) for temporal-trend removal in the first stage and a random forest model used to assess residual spatiotemporal variations in the second stage. We obtained a model-fitting R2 of 0.98 with a root mean square error (RMSE) of 1.40 mu g/m3. The leave-one-out cross-validation (LOOCV) R2 with seasonal stratification was 0.82, and the RMSE was 3.85 mu g/m3, whereas the R2 and RMSE obtained by using the pure random forest approach produced R2 and RMSE values of 0.74 and 4.60 mu g/m3, respectively. The results indicated that the ensemble modeling approach had a higher predictive ability than the pure machine learning method and could provide reliable PM2.5 estimates over the entire island, which has complex terrain in terms of land use and topography.

关键词： Aerosol optical depth generalized additive model Inverse distance weighting Land use regression Leave-one-out cross-validation

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Estimating short-term mortality benefits associated with a reduction in tropospheric ozone

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ATMOSPHERIC ENVIRONMENT 2021年 252卷 118342-118342页

作者： Farzad, Kiarash Khorsandi, Babak Khorsandi, Maziar Bouamra, Omar Maknoon, Reza Amirkabir Univ Technol Dept Civil & Environm Engn 350 Hafez St Tehran *** Iran Univ Washington Div Cardiothorac Surg Med Ctr Seattle WA 98195 USA Univ Manchester Fac Biol Med & Hlth Epidemiol Ctr Manchester Lancs England

Objective: Ground-level ozone (O-3) is an air contaminant and can be harmful to the human health and environment. The objective of this study is to analyze the statistical trend of O-3 and the association between the daily O-3 exposure and mortality in Tehran, and to estimate the short-term health benefits of reducing the concentration of O-3 in the ambient air of Tehran. Methods: The statistical parameters such as the mean, coefficient of variation, skewness, and kurtosis of O-3 concentration for warm and cold seasons were calculated. The association and temporal relationship between the daily O-3 concentration and different causes of mortality were analyzed using the generalized additive model (GAM). We utilized the BenMAP software to estimate the (short-term) avoidable mortality associated with reducing O-3 concentration to the US EPA's standard level (70 ppb) for a full year and the warm seasons only. Results: The statistical analysis of O-3 revealed an increase in the 1-h maximum and 8-h maximum concentrations in recent years. The association between O-3 exposure and mortality was significant for the warm seasons when the concentration was highest. For every 10 ppb increase in the O-3 concentration, there was 1.2% increase in mortality. Furthermore, the association was more significant for cardiovascular than respiratory attributed mortality. The analysis also showed a 3-day lag from the time of O-3 exposure and mortality for the total population. The aforementioned scenario (for a full year) run in BenMAP resulted in 508 (90% CI: 69-937) annual all-cause avoidable deaths in Tehran. Conclusion: The maximum O-3 concentration in Tehran has been increasing in the recent years, raising the alarm for potential adverse health impacts. In this study we found a significant association between short-term exposure to O-3 and mortality during the warm seasons and a substantial predicted health benefit in reduction of this pollutant. Given the increasing ambient O-3 concen

关键词： Air pollution O-3 Health impact assessment generalized additive model BenMAP Tehran

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Terrain and its effects on fog occurrence

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SCIENCE OF THE TOTAL ENVIRONMENT 2021年 768卷 144359-144359页

作者： Hunova, Iva Brabec, Marek Maly, Marek Dumitrescu, Alexandru Geletic, Jan Czech Hydrometeorol Inst Na Sabatce 17 Prague 14306 4 Komorany Czech Republic Charles Univ Prague Fac Sci Inst Environm Studies Benatska 2 Prague 12800 2 Czech Republic Czech Acad Sci Inst Comp Sci Pod Vodarenskou Vezi 2 Prague 18207 8 Czech Republic Natl Inst Publ Hlth Srobarova 48 Prague 10042 10 Czech Republic Meteo Romania Natl Meteorol Adm Dept Climatol Bucharest 013686 Romania

Fog is a very complex phenomenon, relevant to both atmospheric physics and chemistry, contributing to the atmospheric inputs of both nutrients and pollutants to the environment. Fog occurrence is affected by numerous factors. The aim of this study is to examine the effects of terrain on fog occurrence. Namely, we studied in detail how altitude, slope and landform influence the probability of fog occurrence using the generalized additive model. In particular, we investigated how different explanatory variables might modify (deform) the trend and the seasonal component of the probability of fog occurrence. We used long-term records of daily fog occurrence measured in 1981-2017 at 56 professional meteorological stations in Romania, reflecting different environments and geographical areas. The altitude of the sites under review ranged between 13 and 2504 m above sea level, the coverage of localities at different altitudes being highly uneven. Out of the terrain variables considered, the most dedsive influence was found to be altitude. We have included information on slope and landform, which refined and bettered the basic model. Our model results indicated a significant decrease in the probability of fog occurrence over the examined period. The behaviour of fog differed according to the altitude, the most profound effects being observed for ground-level fog and fog above flat terrain. The probability of fog occurrence at different altitudes varied mostly in summer and autumn, whereas it was very similar in winter. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Fog Terrain Romania generalized additive model Trend Seasonality Seasonality deformation 1981-2017 Altitude Slope Curvature

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Effect of short-term ambient PM_2.5 exposure on fasting blood glucose levels: A longitudinal study among 47,471 people in eastern China

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ENVIRONMENTAL POLLUTION 2021年 290卷 117983-117983页

作者： Zhan, Mengyao Li, Zhongqi Li, Xiaona Tao, Bilin Zhang, Qun Wang, Jianming Nanjing Med Univ Ctr Global Hlth Sch Publ Hlth Dept Epidemiol 101 Longmian Ave Nanjing 211166 Peoples R China Nanjing Med Univ Sch Publ Hlth Dept Hlth Management Nanjing 211166 Peoples R China Nanjing Med Univ Affiliated Hosp 1 Hlth Management Ctr Nanjing 210029 Peoples R China

As a common health indicator in physical examinations, fasting blood glucose (FBG) level measurements are widely applied as a diagnostic method for diabetes mellitus. Uncertain conclusions remained regarding the relationship between PM2.5 exposure and FBG levels. We enrolled 47,471 subjects who participated in annual physical examinations between 2017 and 2019. We collected their general characteristics and FBG levels, and environmental factors simultaneously. We applied the generalized additive model to evaluate the impact of short-term outdoor PM2.5 exposure on FBG levels. Among the entire population, the single-pollutant models showed that a 10 mu g/m(3) increase in PM2.5 significantly contributed to 0.0030, 0.0233, and 0.0325 mmol/L increases in FBG at lag 0-7 days, lag 0-21 days, and lag 0-28 days, respectively. Accordingly, in multipollutant models, when PM2.5 increased by 10 mu g/m(3), there was an elevation of 0.0361, 0.0315, 0.0357, and 0.0387 mmol/L in FBG for 8-day, 15-day, 22-day, and 29-day moving averages, respectively. Similarly, we observed a significant positive association between them in the normal population. Moreover, the effects could be modified by age in both the entire and normal populations. Decreasing the ambient PM2.5 concentrations can alleviate the elevation of FBG, which may significantly impact the burden of diabetes mellitus.

关键词： PM2.5 Fasting blood glucose level generalized additive model Longitudinal study

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Extending density surface models to include multiple and double-observer survey data

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PEERJ 2021年 9卷 e12113页

作者： Miller, David L. Fifield, David Wakefield, Ewan Sigourney, Douglas B. Univ St Andrews Ctr Res Ecol & Environm Modelling St Andrews Scotland Univ St Andrews Sch Math & Stat St Andrews Scotland Environm & Climate Change Canada Sci & Technol Branch Wildlife Res Div Mt Pearl NF Canada Univ Glasgow Inst Biodivers Anim Hlth & Comparat Med Glasgow Lanark Scotland Integrated Stat Woods Hole MA USA

Spatial models of density and abundance are widely used in both ecological research (e.g., to study habitat use) and wildlife management (e.g., for population monitoring and environmental impact assessment). Increasingly, modellers are tasked with integrating data from multiple sources, collected via different observation processes. Distance sampling is an efficient and widely used survey and analysis technique. Within this framework, observation processes are modelled via detection functions. We seek to take multiple data sources and fit them in a single spatial model. Density surface models (DSMs) are a two-stage approach: first accounting for detectability via distance sampling methods, then modelling distribution via a generalized additive model. However, current software and theory does not address the issue of multiple data sources. We extend the DSM approach to accommodate data from multiple surveys, collected via conventional distance sampling, double-observer distance sampling (used to account for incomplete detection at zero distance) and strip transects. Variance propagation ensures that uncertainty is correctly accounted for in final estimates of abundance. Methods described here are implemented in the dsm R package. We briefly analyse two datasets to illustrate these new developments. Our new methodology enables data from multiple distance sampling surveys of different types to be treated in a single spatial model, enabling more robust abundance estimation, potentially over wider geographical or temporal domains.

关键词： Density surface model Distance sampling generalized additive model Spatial modelling Variance propagation Abundance estimation

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Forecast and early warning of hand, foot, and mouth disease based on meteorological factors: Evidence from a multicity study of 11 meteorological geographical divisions in mainland China

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ENVIRONMENTAL RESEARCH 2021年 192卷 110301-110301页

作者： Gao, Qi Liu, Zhidong Xiang, Jianjun Tong, Michael Zhang, Ying Wang, Shuzi Zhang, Yiwen Lu, Liang Jiang, Baofa Bi, Peng Shandong Univ Cheeloo Coll Med Sch Publ Hlth Dept Epidemiol 44 Wenhuaxi Rd Jinan 250012 Shandong Peoples R China Shandong Univ Climate Change & Hlth Ctr Jinan Shandong Peoples R China Univ Adelaide Sch Publ Hlth Adelaide SA Australia Fujian Med Univ Sch Publ Hlth Fuzhou 350108 Peoples R China Univ Sydney Sch Publ Hlth China Studies Ctr Sydney NSW Australia Chinese Ctr Dis Control & Prevent Natl Inst Communicable Dis Control & Prevent State Key Lab Infect Dis Prevent & Control Beijing 102206 Peoples R China

Background: Hand, foot, and mouth disease (HFMD) is a significant public health issue in China. Early warning and forecasting are one of the most cost-effective ways for HFMD control and prevention. However, relevant research is limited, especially in China with a large population and diverse climatic characteristics. This study aims to identify local specific HFMD epidemic thresholds and construct a weather-based early warning model for HFMD control and prevention across China. Methods: Monthly notified HFMD cases and meteorological data for 22 cities selected from different climate zones from 2014 to 2018 were extracted from the National Notifiable Disease Surveillance System and the Meteorological Data Sharing Service System, respectively. A generalized additive model (GAM) based on meteorological factors was conducted to forecast HFMD epidemics. The receiver operator characteristic curve (ROC) was generated to determine the value of optimal warning threshold. Results: The developed model was solid in forecasting the epidemic of HFMD with all R square (R-2) in the 22 cities above 85%, and mean absolute percentage error (MAPE) less than 1%. The warning thresholds varied by cities with the highest threshold observed in Shenzhen (n = 7195) and the lowest threshold in Liaoyang (n = 12). The areas under the curve (AUC) was greater than 0.9 for all regions, indicating a satisfied discriminating ability in epidemics detection. Conclusions: The weather-based HFMD forecasting and early warning model we developed for different climate zones provides needed information on occurrence time and size of HFMD epidemics. An effective early warning system for HFMD could provide sufficient time for local authorities to implement timely interventions to minimize the HFMD morbidity and mortality.

关键词： Hand foot and mouth disease generalized additive model Forecasting Early warning Threshold

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Health effect assessment of PM2.5 pollution due to vehicular traffic (case study: Isfahan)

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JOURNAL OF TRANSPORT & HEALTH 2022年 24卷

作者： Soleimani, Mozhgan Akbari, Nematollah Saffari, Babak Haghshenas, Hosein Univ Isfahan Dept Econ Esfahan Iran Isfahan Univ Technol Dept Transportat Engn Esfahan Iran

Introduction: Air pollution is the most challenging issue in megacities worldwide. Isfahan is the third most populated city in Iran, and its pollution is a leading cause of many diseases and deaths, and economic problems every year. Root cause analysis of air pollution can help the government and legislators make laws for reducing air pollution. As critical factors play an important role in fine particulate matter (PM2.5) emission, traffic parameters should be essential in air pollution modeling. Methods: The generalized additive model (GAM), as a flexible statistical model, is implemented to demonstrate non-linear and linear relationships between response and predictive variables by nonparametric smooth functions. Traffic counts and meteorological and spatial variables were considered independent variables in this research. Regarding evaluating the health outcomes of air pollution due to traffic, the AirQ+ model is implemented. Results: This research was applied for Isfahan in the two years from 2018 to 2020 based on experimental data. The results indicated that the models could explain 54%-69% of PM2.5 concentrations in different areas. In Isfahan's southern and central regions, the effect of traffic on PM2.5 pollution was more. Low values of temperature also affected concentration pollution. Moreover, the model was estimated seasonally for further investigation. Also, annual premature mortality attributed to PM2.5 due to traffic is 150 and 157 cases over two years. Conclusions: The results show the health impacts of air pollution due to vehicular PM2.5 emissions are remarkable.

关键词： Air pollution Fine particulate matter generalized additive model Vehicular traffic AirQ(+) Health effects

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Impact of climate change on the fishery of Indian mackerel (Rastrelliger kanagurta) along the Kerala coast off the southeastern Arabian Sea

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REGIONAL STUDIES IN MARINE SCIENCE 2021年 44卷

作者： Hussain, Sajna Valiyakath Ulahannan, Zacharia Pariyappanal Joseph, Dhanya Somasekharan, Akash Girindran, Rojith Benny, Sharon Ninan, Roshen George Valappil, Sathianandan Thayyil ICAR Cent Marine Fisheries Res Inst Cochin 682018 Kerala India

Climate change impact varies at regional as well as species level and accordingly research needs to be focused on exploring the region-wise influence of oceanographic variations on key marine species. The present study depicts the annual and decadal fluctuations in Indian mackerel fishery along Kerala coast of southeastern Arabian Sea over a time period of 31 years (1985-2016), its relationship between four major oceanographic variables (SST, Pr, SSS and SSC) and future predictions under two Representative Concentration Pathways (RCPs). Coast specific changes of these oceanographic variables since 1960 provide a baseline status of the existing climatic conditions of the marine ecosystem of the SEAS whereas, the RCP projections till 2100 provide an insight to the future conditions. generalized additive models (GAMs) has been used to comprehend the relation among the Weighted CPUE (cpue) and Relative effort (Effort) of Indian mackerel and the four major oceanographic variables. The GAM model could explain 68.9 % and 71.1 % deviance of cpue and Effort respectively. The significance of the most influential oceanographic variables on the cpue of Indian mackerel were in the order SST > Pr > SSC > SSS and for Effort were in the order Pr > SST > SSS > SSC. The projected cpue and Effort of Indian mackerel exhibits varying trends under the RCP 4.5 and RCP 6.0 scenarios. The future catch potential of Indian mackerel predicted under both RCP scenarios for the period 2020-2100 also show reduction, with the latter exhibiting a more drastic reduction. The study implies that for sustainable long-term fishery and to reduce the impact of climate change on Indian mackerel, the fishing pressure has to be maintained at low and healthy level. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Climate change CPUE generalized additive model Indian mackerel Kerala coast RCP scenarios

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