Evaluation of MARS for the spatial distribution modeling of carbon monoxide in an urban area

Spatial distribution modeling of CO in Tehran can lead to better air pollution management and control, and it is also suitable for exposure assessment and epidemiological studies. In this study MARS (Multi–variate Adaptive Regression Splines) is compared with typical interpolation techniques for spatial distribution modeling of hourly and daily CO concentrations in Tehran, Iran. The measured CO data in 2008 by 16 monitoring stations were used in this study. The Generalized Cross Validation (GCV) and Cross Validation techniques were utilized for the parameter optimization in the MARS and other techniques, respectively. Then the optimized techniques were compared based on the mean absolute of percentage error (MAPE). Although the Cokriging technique presented less MAPE than the Inverse Distance Weighting, Thin Plate Smooth Splines and Kriging techniques, MARS exhibited the least MAPE. In addition, the MARS modeling procedure is easy. Therefore, MARS has merit to be introduced as an appropriate method for spatial distribution modeling. The number of air pollution monitoring stations is very low (16 stations for 22 zones) and the distribution of stations is not suitable for spatial estimation, hence the level of errors was relatively high (more than 60%). Consequently, hourly and daily mapping of CO provides a limited picture of spatial patterns of CO in Tehran, but it is suitable for estimation of relative CO levels in different zones of Tehran. Hence, the map of mean annual CO concentration was generated by averaging daily CO distributions in 2008. It showed that the most polluted regions in Tehran are the central, eastern and southeastern parts, and mean annual CO concentration in these parts (zones 6, 12, 13, 14 and 15) is between 4.2 and 4.6 ppm

On the Use of Mobile Sensors for Estimating City-Wide Pollution Levels

©2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Obtaining detailed pollution maps for urban environments is an effort that is gathering much interest by allowing to better regulate traffic and protect citizens from hazardous conditions. However, the scarcity of pollution sensors prevents obtaining the desired degree of detail, requiring alternative solutions to be deployed. In this paper we explore the concept of mobile pollution sensing by studying the feasibility of equipping buses with ozone measurement hardware to estimate ozone patterns for the city of Compiègne. Overall, we achieve accurate estimations, with error values typically ranging from 2% to 10%. Compared to solutions based on deploying static sensors on the different bus stops available, we find that the proposed mobile sensing approach is able to provide a degree of accuracy comparable to deploying tens of static sensors, substantially reducing costs and management.This work was carried out and funded in the framework of the Labex MSZT. It was supported by the French Government, through the program "Investments for the future” managed by the National Agency for Research (Reference ANR-11-IDEX-0004-02). This work was partially funded by the Celtic Plus CoMoSeF project "Cooperative mobility for the services of the future". The authors thank the Agglomération de la Région de Compiégne (ARC).Tavares De Araujo Cesariny Calafate, CM.; Ducourthial, B. (2015). On the Use of Mobile Sensors for Estimating City-Wide Pollution Levels. IEEE. doi:10.1109/IWCMC.2015.7289093

Optimizing peak gust and maximum sustained wind speed estimates from mid-latitude wave cyclones

Wind storms cause significant damage and economic loss and are a major recurring threat in many countries. Maximum sustained and peak gust weather station data from multiple historic wind storms occurring over more than three decades across Europe were analyzed to identify storm tracks, intensities, and areas of frequent high wind speeds. Wind surfaces for maximum sustained and peak gust winds were estimated based on an anisotropic (directionally-dependent) kriging interpolation methodology. Overall, wind speed magnitudes and high intensity locations were identified accurately for each storm. Directional trends and wind swaths were also consistently located in appropriate locations based on known storm tracks. Anisotropic kriging proved to be superior to isotropic (non-directional) kriging when modeling continental-scale wind storms because of the identification of strong directional correlations across space. Results suggest that coastal areas and mountainous areas experience the highest wind intensities during wind storms. These same areas also experience high variability over short distances and thus the highest error measurements associated with concurrent interpolated surfaces. For this reason, various covariates were utilized in conjunction with the cokriging interpolation technique and improved the interpolated wind surfaces for five wind storms that impacted both the mountainous and topographically-varied Alps region and the coastal regions of Europe. Land cover alone reduced station-measured standard error most significantly in a majority of the models, while aspect and elevation (singularly and collectively) also reduced station standard error in most models as compared to the original kriging models. Additional comparisons between different areal scales of kriging/cokriging models revealed that some surface wind variability is muted at the continental scale, but identifiable at the local scale. However, major patterns and trends are more difficult to ascertain for local-scale surfaces when compared to continental-scale surfaces. Large station error can be reduced through local kriging/cokriging, but additional research is needed to merge local-scale semivariograms with continental-scale models. Results showed substantial improvements in wind speed surface estimates over previous estimates and have major implications for catastrophe modeling companies, insurance needs, and construction standards. Implications of this research may be transferrable to other geographies and create an impetus for database and covariate improvement

A Review of 21st-Century Studies

PM10 prediction has attracted special legislative and scientific attention due to its harmful effects on human health. Statistical techniques have the potential for high-accuracy PM10 prediction and accordingly, previous studies on statistical methods for temporal, spatial and spatio-temporal prediction of PM10 are reviewed and discussed in this paper. A review of previous studies demonstrates that Support Vector Machines, Artificial Neural Networks and hybrid techniques show promise for suitable temporal PM10 prediction. A review of the spatial predictions of PM10 shows that the LUR (Land Use Regression) approach has been successfully utilized for spatial prediction of PM10 in urban areas. Of the six introduced approaches for spatio-temporal prediction of PM10, only one approach is suitable for high-resolved prediction (Spatial resolution < 100 m; Temporal resolution ¤ 24 h). In this approach, based upon the LUR modeling method, short-term dynamic input variables are employed as explanatory variables alongside typical non-dynamic input variables in a non- linear modeling procedure

An approach to predict population exposure to ambient air PM2.5 concentrations and its dependence on population activity for the megacity London

© 2019 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/.A comprehensive modelling approach has been developed to predict population exposure to the ambient air PM2.5 concentrations in different microenvironments in London. The modelling approach integrates air pollution dispersion and exposure assessment, including treatment of the locations and time activity of the population in three microenvironments, namely, residential, work and transport, based on national demographic information. The approach also includes differences between urban centre and suburban areas of London by taking account of the population movements and the infiltration of PM2.5 from outdoor to indoor. The approach is tested comprehensively by modelling ambient air concentrations of PM2.5 at street scale for the year 2008, including both regional and urban contributions. Model analysis of the exposure in the three microenvironments shows that most of the total exposure, 85%, occurred at home and work microenvironments and 15% in the transport microenvironment. However, the annual population weighted mean (PWM) concentrations of PM2.5 for London in transport microenvironments were almost twice as high (corresponding to 13-20 µg/m3) as those for home and work environments (7-12 µg/m3). Analysis has shown that the PWM PM2.5 concentrations in central London were almost 20% higher than in the surrounding suburban areas. Moreover, the population exposure in the central London per unit area was almost three times higher than that in suburban regions. The exposure resulting from all activities, including outdoor to indoor infiltration, was about 20% higher, when compared with the corresponding value obtained assuming inside home exposure for all times. The exposure assessment methodology used in this study predicted approximately over one quarter (-28%) lower population exposure, compared with using simply outdoor concentrations at residential locations. An important implication of this study is that for estimating population exposure, one needs to consider the population movements, and the infiltration of pollution from outdoors to indoors.Peer reviewedFinal Accepted Versio

Improving public health in smart cities in the air pollution context

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesThe public has continually developed interest in knowing the air quality around them. This is of great importance not only for planning their activities, but also for taking precautionary measures for their health. With support from smart cities infrastructure that supports taking measurements of pollutant concentrations, several countries and researchers have used the concept of air quality index (AQI) in its different forms of air quality or air pollution to interpret and communicate such measurements. In this study we have reviewed the implemented indices by government bodies and some formulations from researchers in relation to the available data to determine an optimum index for Madrid city. This comparison has helped to formulate the Madrid Local Air Quality Index (MLAQI), which considers the local situation in Madrid city. In relation to the available data from the city council, we have reviewed and compared some of the spatial interpolation methods that have been applied in the field of air pollution. This helped us to identify IDW for support of automated hourly pollution interpolation for the available data from Madrid pollution sensors. We have then used MLAQI and IDW to create an hourly pollution Web Feature service aimed at helping with public awareness of the air quality around them. The surfaces are categorised with the index categories from good to very poor categories with defined colour coding. We used the created service to develop a routing web application where high MLAQI categories of poor and very poor are used as polygon barriers to limit the route calculation in those polluted areas thereby helping the public to protect their health from such areas

Representing past and future hydro-climatic variability over multi-decadal periods in poorly-gauged regions: the case of Ecuador

Cette thèse évalue des méthodes pour représenter la variabilité spatio-temporelle hydro-climatique passée et future dans les régions peu jaugées. Elle propose une procédure complète et reproductible appliquée à l'Équateur et s'appuyant sur des données hydro-climatiques observées et simulées en vue de représenter la variabilité passée et de projeter l'impact potentiel des changements climatiques sur les écoulements à la fin du 21ème siècle. Un état de l'art a permis d'identifier plusieurs techniques qui ont été intégrées dans une chaîne méthodologique pour obtenir des séries spatio-temporelles continues de température, de précipitation et de débit sur les périodes multi-décennales passées et futures. Trois chapitres centraux sont consacrés à cet objectif selon les thèmes suivants : (1) régionalisation de la température et des précipitations à partir de mesures in situ en comparant des techniques déterministes et géostatistiques avec une prise en compte de corrections orographiques; (2) reconstruction du débit dans différents bassins versants à l'aide de modèles hydrologiques conceptuels utilisés selon une approche multimodèle et multiparamétrique; et (3) projections hydro-climatiques basées sur des simulations de modèles climatiques sous contrainte d'un scénario marqué d'émission de gaz à effet de serre. La régionalisation du climat a révélé l'importance de caler les paramètres de spatialisation et d'évaluer les champs interpolés par rapport à des stations ponctuelles indépendantes et via des analyses de sensibilité hydrologique. La reconstruction des débits a été possible grâce aux simulations combinées de trois modèles hydrologiques évalués dans des conditions climatiques contrastées, et forcés par les variables climatiques régionalisées. Des simulations de changements hydro-climatiques à moyen terme (2040-2070) et à long terme (2070-2100) ont ensuite été analysées avec des intervalles de confiance de 95 %, en utilisant des scénarios de neuf modèles climatiques et en transférant les paramètres hydrologiques calibrés pour la reconstruction des débits. L'analyse de la variabilité hydro-climatique montre une légère augmentation des températures sur la période 1985-2015, tandis que la variabilité des précipitations est liée aux principaux modes des phases El Niño et La Niña à l'échelle inter-annuelle et au déplacement de la zone de convergence inter-tropicale (ZCIT) à l'échelle saisonnière. Une augmentation générale de la température (+4,4 °C) et des précipitations (+17 %) est attendue d'ici à la fin du 21ème siècle, ce qui pourrait entraîner une augmentation de +5 % à +71 % du débit annuel moyen selon les bassins versants. Ces résultats sont discutés en termes d'importance pour la gestion de l'eau, avant de suggérer de futures recherches hydrologiques telles que la régionalisation du débit des cours d'eau, une meilleure quantification des incertitudes et une évaluation de la capacité à satisfaire les futurs besoins en eau.This thesis investigates methods to represent the past and future hydro-climatic variability in space and over time in poorly-gauged regions. It proposes a complete and reproducible procedure applied to the continental Ecuador to deal with observed and simulated hydro-climatic data in order to represent past variability and project the potential impact of climate change on water resources by the end of the 21st century. Up-to-date techniques were identified in a literature review and were integrated in a chain protocol to obtain continuous space-time series of air temperature, precipitation and streamflow over past and future multi-decadal periods. Three central chapters are dedicated to this objective according to the following topics: (1) regionalization of air temperature and precipitation from in situ measurements by comparing deterministic and geostatistical techniques including orographic corrections; (2) streamflow reconstruction in various catchments using conceptual hydrological models in a multi-model, multi-parameter approach; and (3) hydro-climate projections using climate model simulations under a high range emission scenario. Climate regionalization revealed the importance of calibrating parameters and of assessing interpolated fields against independent gauges and via hydrological sensitivity analyses. Streamflow reconstruction was possible with the regionalized climate inputs and the combined simulations of three hydrological models evaluated in contrasting climate conditions. Future medium term (2040-2070) and long term (2070-2100) hydro-climatic changes were analysed with confidence intervals of 95% using scenarios from nine climate models and transferring the model parameters calibrated for streamflow reconstruction. Analysis of hydro-climatic variability over the period 1985-2015 showed a slight increase in temperature, while precipitation variability was linked to the main modes of El Niño and La Niña phases at inter-annual scale and to the displacement of the inter-tropical convergence zone (ITCZ) at seasonal scale. Under climate change, a general increase in temperature (+4.4 °C) and precipitation (+17%) is expected by the end of the 21st century, which could lead to between +5% and 71% increase in mean annual streamflow depending on the catchments. These results are discussed in terms of significance for water management before suggesting future hydrological research such as regionalizing streamflow, better quantifying uncertainties and assessing the capacity to meet future water requirements

Current Air Quality Issues

Air pollution is thus far one of the key environmental issues in urban areas. Comprehensive air quality plans are required to manage air pollution for a particular area. Consequently, air should be continuously sampled, monitored, and modeled to examine different action plans. Reviews and research papers describe air pollution in five main contexts: Monitoring, Modeling, Risk Assessment, Health, and Indoor Air Pollution. The book is recommended to experts interested in health and air pollution issues

Space-time exposure modelling of troposheric O3 in Europe

Exposure models need to be developed which can be applied at the continental scale, while still reflecting local variations in exposure conditions. Land use regression (LUR) has been widely adopted to describe the spatial variations in air pollutants over the longer term but not for short-term time-variable exposures. This study, therefore, aimed to develop and validate a space-time O3 model applicable to epidemiological studies investigating the health effects of short-term (e.g. daily) O3 exposures at the small-area scale. A geographical information system (GIS) was developed, incorporating data from 1211 O3 monitoring sites across Western Europe and a range of predictors, stored as 100m grids, including land cover, roads, topography and meteorology. The spatial model consisted of a LUR model representing the long-term average for years 2001-2007. The monitoring sites were classified, using multivariate statistical techniques, into 13 site types based on a set of descriptive indicators, then 13 temporal models represented by time functions were produced – one for each site type. These were linked to the spatial model using probability of group membership as a weighting factor. Finally, local meteorological data were incorporated to produce the full space-time model to predict daily concentrations for point locations. The spatial and temporal models were individually evaluated based on agreement with measurement data from a reserved subset of 20% of the monitoring sites. The performance of the spatial model was similar to other continental LUR models (R2=0.67; RMSE=7.64 μg/m3), while performance of the temporal models ranged from 0.3 to 0.5 (R2). Including local meteorological data into the full spatial-temporal model improved correlation with the concentrations measured at 30 monitoring sites in the Netherlands (R2= 0.42 without; R2=0.53 with meteorology). Modelling daily O3 over large areas at a fine spatial scale is possible using this approach. Overall model performance was further improved as the temporal period was aggregated to weekly or monthly. The model was applied to mothers in two birth cohorts in the European Study of Cohorts for Air Pollution Effects (ESCAPE) to provide daily O3 exposure estimates, which can be aggregated as needed to provide individualised exposures based on date of birth