Spatial modeling of air pollution based on data streams from geosensor networks

2018 ◽  
Vol 930 (12) ◽  
pp. 39-43 ◽  
Author(s):  
V.P. Savinikh ◽  
A.A. Maiorov ◽  
A.V. Materuhin
Keyword(s):  
Air Pollution ◽  
Data Streams ◽  
Spatial Modeling ◽  
Spatial Model ◽  
Large City ◽  
Field Tests ◽  
Processing Unit ◽  
Geosensor Network ◽  
Spatio Temporal ◽  
Different Sources

The article is a brief summary of current research results of the authors in the field of spatial modeling of air pollution based on spatio-temporal data streams from geosensor networks. The urban environment is characterized by the presence of a large number of different sources of emissions and rapidly proceeding processes of contamination spread. So for the development of an adequate spatial model is required to make measurements with a large spatial and temporal resolution. It is shown that geosensor network provide researchers with the opportunity to obtain data with the necessary spatio-temporal detail. The article describes a prototype of a geosensor network to build a detailed spatial model of air pollution in a large city. To create a geosensor in the prototype of the system, calibrated gas sensors for a nitrogen dioxide and carbon monoxide concentrations measurement were interfaced to the module, which consist of processing unit and communication unit. At present, the authors of the article conduct field tests of the prototype developed.

Performance Modeling of Spatio-Temporal Algorithms Over GEDS Framework

2012 ◽  
Vol 4 (3) ◽  
pp. 63-84
Author(s):  
Jonathan Cazalas ◽  
Ratan K. Guha
Keyword(s):  
Parallel Processing ◽  
Data Streams ◽  
Graphics Processing Unit ◽  
Performance Model ◽  
Processing Unit ◽  
Data Streaming ◽  
Temporal Data ◽  
Processing Power ◽  
Spatio Temporal ◽  
Graphics Processing

The efficient processing of spatio-temporal data streams is an area of intense research. However, all methods rely on an unsuitable processor (Govindaraju, 2004), namely a CPU, to evaluate concurrent, continuous spatio-temporal queries over these data streams. This paper presents a performance model of the execution of spatio-temporal queries over the authors’ GEDS framework (Cazalas & Guha, 2010). GEDS is a scalable, Graphics Processing Unit (GPU)-based framework, employing computation sharing and parallel processing paradigms to deliver scalability in the evaluation of continuous, spatio-temporal queries over spatio temporal data streams. Experimental evaluation shows the scalability and efficacy of GEDS in spatio-temporal data streaming environments and demonstrates that, despite the costs associated with memory transfers, the parallel processing power provided by GEDS clearly counters and outweighs any associated costs. To move beyond the analysis of specific algorithms over the GEDS framework, the authors developed an abstract performance model, detailing the relationship of the CPU and the GPU. From this model, they are able to extrapolate a list of attributes common to successful GPU-based applications, thereby providing insight into which algorithms and applications are best suited for the GPU and also providing an estimated theoretical speedup for said GPU-based applications.

scholarly journals Childhood Leukemia in Small Geographical Areas and Proximity to Industrial Sources of Air Pollutants in Three Colombian Cities

2020 ◽  
Vol 17 (21) ◽  
pp. 7925
Author(s):  
Laura Andrea Rodriguez-Villamizar ◽  
Feisar Enrique Moreno-Corzo ◽  
Ana María Valbuena-Garcia ◽  
Claudia Janeth Uribe Pérez ◽  
Mary Ruth Brome Bohórquez ◽  
...  
Keyword(s):  
Air Pollution ◽  
Spatial Modeling ◽  
Air Pollutants ◽  
Childhood Leukemia ◽  
Cancer Registries ◽  
Incidence Rates ◽  
Environmental Carcinogens ◽  
Capital Cities ◽  
Industrial Sources ◽  
Spatio Temporal

Acute leukemia is the most common childhood cancer and has been associated with exposure to environmental carcinogens. This study aimed to identify clusters of acute childhood leukemia (ACL) cases and analyze their relationship with proximity to industrial sources of air pollution in three capital cities in Colombia during 2000–2015. Incident ACL cases were obtained from the population cancer registries for the cities of Bucaramanga, Cali, and Medellín. The inventory of industrial sources of emissions to the air was obtained from the regional environmental authorities and industrial conglomerates were identified. The Kulldorf’s circular scan test was used to detect city clusters and to identify clusters around industrial conglomerates. Multivariable spatial modeling assessed the effect of distance and direction from the industrial conglomerates controlling for socioeconomic status. We identified industrials sectors within a buffer of 1 km around industrial conglomerates related to the ACL clusters. Incidence rates showed geographical heterogeneity with low spatial autocorrelation within cities. The spatio-temporal tests identified one cluster in each city. The industries located within 1 km around the ACL clusters identified in the three cities represent different sectors. Exposure to air pollution from industrial sources might be contributing to the incidence of ACL cases in urban settings in Colombia.

scholarly journals Air pollution modelling using a Graphics Processing Unit with CUDA

2010 ◽  
Vol 181 (1) ◽  
pp. 105-112 ◽  
Author(s):  
F. Molnár ◽  
T. Szakály ◽  
R. Mészáros ◽  
I. Lagzi
Keyword(s):  
Air Pollution ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Air Pollution Modelling ◽  
Graphics Processing

How low-cost air pollution sensors could make homes smarter?

2021 ◽  
Author(s):  
Hamid Omidvarborna ◽  
Prashant Kumar
Keyword(s):  
Air Pollution ◽  
Indoor Air ◽  
Air Pollutants ◽  
Indoor Air Pollution ◽  
Prediction Models ◽  
Disease Risk ◽  
Low Cost ◽  
Mortality And Morbidity ◽  
Spatio Temporal ◽  
Indoor Air Pollutants

<p>The majority of people spend most of their time indoors, where they are exposed to indoor air pollutants. Indoor air pollution is ranked among the top ten largest global burden of a disease risk factor as well as the top five environmental public health risks, which could result in mortality and morbidity worldwide. The spent time in indoor environments has been recently elevated due to coronavirus disease 2019 (COVID-19) outbreak when the public are advised to stay in their place for longer hours per day to protect lives. This opens an opportunity to low-cost air pollution sensors in the real-time Spatio-temporal mapping of IAQ and monitors their concentration/exposure levels indoors. However, the optimum selection of low-cost sensors (LCSs) for certain indoor application is challenging due to diversity in the air pollution sensing device technologies. Making affordable sensing units composed of individual sensors capable of measuring indoor environmental parameters and pollutant concentration for indoor applications requires a diverse scientific and engineering knowledge, which is not yet established. The study aims to gather all these methodologies and technologies in one place, where it allows transforming typical homes into smart homes by specifically focusing on IAQ. This approach addresses the following questions: 1) which and what sensors are suitable for indoor networked application by considering their specifications and limitation, 2) where to deploy sensors to better capture Spatio-temporal mapping of indoor air pollutants, while the operation is optimum, 3) how to treat the collected data from the sensor network and make them ready for the subsequent analysis and 4) how to feed data to prediction models, and which models are best suited for indoors.</p>

scholarly journals User-Centred Design of a Final Results Report for Participants in Multi-Sensor Personal Air Pollution Exposure Monitoring Campaigns

2021 ◽  
Vol 18 (23) ◽  
pp. 12544
Author(s):  
Johanna Amalia Robinson ◽  
Rok Novak ◽  
Tjaša Kanduč ◽  
Thomas Maggos ◽  
Demetra Pardali ◽  
...  
Keyword(s):  
Air Pollution ◽  
Design Thinking ◽  
Low Cost ◽  
Personal Exposure ◽  
Final Report ◽  
Draft Report ◽  
Horizon 2020 ◽  
User Centred Design ◽  
Exposure Monitoring ◽  
Spatio Temporal

Using low-cost portable air quality (AQ) monitoring devices is a growing trend in personal exposure studies, enabling a higher spatio-temporal resolution and identifying acute exposure to high concentrations. Comprehension of the results by participants is not guaranteed in exposure studies. However, information on personal exposure is multiplex, which calls for participant involvement in information design to maximise communication output and comprehension. This study describes and proposes a model of a user-centred design (UCD) approach for preparing a final report for participants involved in a multi-sensor personal exposure monitoring study performed in seven cities within the EU Horizon 2020 ICARUS project. Using a combination of human-centred design (HCD), human–information interaction (HII) and design thinking approaches, we iteratively included participants in the framing and design of the final report. User needs were mapped using a survey (n = 82), and feedback on the draft report was obtained from a focus group (n = 5). User requirements were assessed and validated using a post-campaign survey (n = 31). The UCD research was conducted amongst participants in Ljubljana, Slovenia, and the results report was distributed among the participating cities across Europe. The feedback made it clear that the final report was well-received and helped participants better understand the influence of individual behaviours on personal exposure to air pollution.

scholarly journals Spatio-Temporal Analysis of PM10 in Southern Peninsular Malaysia

2018 ◽  
Vol 7 (3.9) ◽  
pp. 27
Author(s):  
Mohamad Saiful Mohamad Khir ◽  
Khalida Muda ◽  
Norelyza Hussein ◽  
Mohd Faisal Abdul Khanan ◽  
Mohd Nor Othman ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Pollutant Dispersion ◽  
Temporal Analysis ◽  
Peninsular Malaysia ◽  
Inverse Distance Weighted ◽  
Distance Weighted ◽  
Haze Episode ◽  
Spatio Temporal ◽  
Inverse Distance

In this study, the particulate matter with diameter less than 10 micrometers (PM10) is being observed. Other factors that influenced the pollutant dispersion are also being studied prior to identification of their relationship. The aim of this study is to identify the trend of PM10 concentrations in the Southern Peninsular of Malaysia during the period 2005 to 2015 by using spatio-temporal analysis in regards to air pollution. The inverse distance weighted (IDW) is used for the spatio interpolation data and mapping. The trends of the PM10 concentration are illustrated via map which indicates the affected and vulnerable area of Southern Peninsular Malaysia especially during Haze episode.  

scholarly journals Unlocking the Predictive Power of Heterogeneous Data to Build an Operational Dengue Forecasting System

2020 ◽  
Author(s):  
Carrie Manore ◽  
Geoffrey Fairchild ◽  
Amanda Ziemann ◽  
Nidhi Parikh ◽  
Katherine Kempfert ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Data Streams ◽  
Cumulative Risk ◽  
State Level ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Global Dynamics ◽  
Flexible System ◽  
Clinical Surveillance ◽  
Spatio Temporal

ABSTRACTPredicting an infectious disease can help reduce its impact by advising public health interventions and personal preventive measures. While availability of heterogeneous data streams and sensors such as satellite imagery and the Internet have increased the opportunity to indirectly measure, understand, and predict global dynamics, the data may be prohibitively large and/or require intensive data management while also requiring subject matter experts to properly exploit the data sources (e.g., deriving features from fundamentally different data sets). Few efforts have quantitatively assessed the predictive benefit of novel data streams in comparison to more traditional data sources, especially at fine spatio-temporal resolutions. We have combined multiple traditional and non-traditional data streams (satellite imagery, Internet, weather, census, and clinical surveillance data) and assessed their combined ability to predict dengue in Brazil’s 27 states on a weekly and yearly basis over seven years. For each state, we nowcast dengue based on several time series models, which vary in complexity and inclusion of exogenous data. We also predict yearly cumulative risk by municipality and state. The top-performing model and utility of predictive data varies by state, implying that forecasting and nowcasting efforts in the future may be made more robust by and benefit from the use of multiple data streams and models. One size does not fit all, particularly when considering state-level predictions as opposed to the whole country. Our first-of-its-kind high resolution flexible system for predicting dengue incidence with heterogeneous (and still sometimes sparse) data can be extended to multiple applications and regions.

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