Big data and human biosensors – birthweight as a biomonitor for air pollution levels. A population based study using routine data.

IntroductionIn the natural sciences biomonitors, (organisms, such as pine needles, shells, lichen, that can provide quantitative information on the quality of their past and present environments) have been developed for environmental measurement. In recent year’s physiologists have started to explore if human tissue could also be used. Objectives and ApproachThe costs of collecting and processing human tissues for biomonitoring, may be too prohibitive for its use in wide scale monitoring. In contrast if biomonitors could be identified within a routine data collection process that were part of standard medical recording a low cost, widely available large sample would be available to scientists. Pregnancy is known to be effected by air pollution, therefore we explore whether birthweight, recorded in maternity records, could be a biomonitor for air pollution. ResultsWe use maternity records (~1 million births) in Scotland between 2000 and 2015. We modelled, at the individual mother level birthweight, controlling mother’s age, estimated household income, local area crime rates and area level of multiple deprivation. We then aggregate and calculate the averaged of the residuals for this model for all mothers within an intermediate datazones (small areas of around 4000 residents). These mean deviations were then compared with pollution modelled figures produced by AEA for the Scottish government averaged over the same spatial units and time period as the maternity data. "We find a relatively strong correlations (between -0.37 and -0.39) between our ‘biomonitor estimates’ of air pollution derived from the maternity records and the entirely separated modelled air pollution data." Conclusion/ImplicationsAs far as we know this is the first study to demonstrate that it may be possible to use routine health data to derive ‘biomonitors’ information. Importantly if this method proves to be reliable it will be a relatively cheap method for collecting information that is actually personally monitoring.

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Low-cost airbox network and Gaussian plume modelling to assist air quality policy-making at the local scale: Shexian County, Hebei Province, China

10.5194/egusphere-egu21-7296 ◽

2021 ◽

Author(s):

Jun Zhang ◽

Arjan Hensen ◽

Paul Seignette ◽

Dan Yu

Keyword(s):

Air Pollution ◽

Air Quality ◽

Low Cost ◽

Monitoring Network ◽

Local Scale ◽

Hebei Province ◽

Steel Company ◽

Pollution Levels ◽

Gaussian Plume ◽

Coking Plant

High air pollution levels pose a threat to both human health and ecosystem vitality in Hebei Province, NE China. Although air quality changes are monitored hourly with high-end equipment at the provincial scale (197 stations for 187,693 km2) it is difficult for individual counties or cities to improve local air quality based on regional-scale information. The Sino-Dutch Technology Transfer & Training Project established a monitoring network of 43 low-cost air-boxes and 11 standard meteorological stations in Shexian county, Handan city (~ 1500 km2) to measure atmospheric concentrations of PM10, PM2.5, CO, SO2, NO2 and O3 at 1-min intervals from January 2020 onwards. Data from these stations were evaluated in real time using the TNO Gaussian plume model. The model provides point emission levels of PM10, PM2.5 and CO at 10-min intervals after calibration against measured concentrations. Based on a 2019 pollution source inventory, 21 major source areas were identified and used to derive an optimized source map for model input &#8211; including a large steel company, a coal-fueled power plant, different industrial complexes (cement, coking plant for ore smelting), as well as the densely populated city centre, rural residential areas, and a busy highway. The model performs source optimization using concentration data for all 43 stations and subsequently calculates the contributions of individual sources for each monitoring station to see to what extent the source map explained observed concentrations. Full network operation started in July 2020. Based on a one-month test period (August 2020), the steel company and coking plant were estimated to contribute ~25% of the total area&#8217;s PM-emissions. The central city area contributed ~10% and 17% of total PM- and CO-emissions, respectively, mostly due to construction activity and traffic. Repeating the exercise for the two provincial monitoring stations that also had high-end equipment in place in the downtown area gave inferred average urban contributions to measured concentrations as high as 60&#8211;62.5% for PM10 and PM2.5 versus 48% for CO. The steel factory contributed an estimated 9&#8211;11% for PM10 and PM2.5 at these locations and a cement factory 13% for CO. The combined results underline the importance of taking spatial variability of emission sources into explicit account in complex industrialized cities. Moreover, the combination of a low-cost airbox real-time monitoring network with emission apportionment modeling will allow local policy-makers to take proper actions towards reducing air pollution levels at the local scale.

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Monitoring Excess Exposure to Air Pollution for Professional Drivers in London Using Low-Cost Sensors

Atmosphere ◽

10.3390/atmos11070749 ◽

2020 ◽

Vol 11 (7) ◽

pp. 749

Author(s):

Louise Bøge Frederickson ◽

Shanon Lim ◽

Hugo Savill Russell ◽

Szymon Kwiatkowski ◽

James Bonomaully ◽

...

Keyword(s):

Air Pollution ◽

Low Cost ◽

Sensor Nodes ◽

Urban Background ◽

Pm 2.5 ◽

Pollution Levels ◽

Significant Difference ◽

Oak Park ◽

Pm 10 ◽

Pollution Exposure

In this pilot study, low-cost air pollution sensor nodes were fitted in waste removal trucks, hospital vans and taxis to record drivers’ exposure to air pollution in Central London. Particulate matter (PM 2.5 and PM 10 ), CO 2 , NO 2 , temperature and humidity were recorded in real-time with nodes containing low-cost sensors, an electrochemical gas sensor for NO 2 , an optical particle counter for PM 2.5 and PM 10 and a non-dispersive infrared (NDIR) sensor for CO 2 , temperature and relative humidity. An intervention using a pollution filter to trap PM and NO 2 was also evaluated. The measurements were compared with urban background and roadside monitoring stations at Honor Oak Park and Marylebone Road, respectively. The vehicle records show PM and NO 2 concentrations similar to Marylebone Road and a higher NO 2 -to-PM ratio than at Honor Oak Park. Drivers are exposed to elevated pollution levels relative to Honor Oak Park: 1.72 μ g m − 3 , 1.92 μ g m − 3 and 58.38 ppb for PM 2.5 , PM 10 , and NO 2 , respectively. The CO 2 levels ranged from 410 to over 4000 ppm. There is a significant difference in average concentrations of PM 2.5 and PM 10 between the vehicle types and a non-significant difference in the average concentrations measured with and without the pollution filter within the sectors. In conclusion, drivers face elevated air pollution exposure as part of their jobs.

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Development of a Low-Cost Sensor Network for Community-Made Measurements of Air Pollution

Volume 6: Design, Systems, and Complexity ◽

10.1115/imece2020-23994 ◽

2020 ◽

Author(s):

Sebastian Sandoval Campos ◽

Fabián A. Ballesteros Higuera ◽

Sebastián Roa Prada ◽

Claudia I. Cáceres Becerra ◽

Alfredo A. Díaz Claro

Keyword(s):

Air Pollution ◽

Sensor Network ◽

Fossil Fuels ◽

Low Cost ◽

Independent Solution ◽

Weather Conditions ◽

Local Area ◽

Daily Basis ◽

Processing Plant ◽

Communication Module

Abstract The levels of pollution present in the air have been dramatically increasing over the years due to the continuous emission of greenhouse gases such as CO2, CO, NOx and H2S, among others. The main source of these emissions is from burning fossil fuels for electricity, heat, and transportation. This represents a tremendous risk to the populations located near the emission sources where people get exposed to dangerous concentrations of such gases on a daily basis. The lack of open real-time monitoring tools makes people unaware of the damage these pollutants cause to their health. This research proposes the development and implementation of a low-cost independent solution to keep the members of a community informed about concentration levels of air pollution due to local emissions. This tool must be easily accessible to the users so that the data about the number of particulates per million of a specific gas within a zone of interest can be viewed at any time. The proposed solution consists of a sensor network, covering the widest possible area, with respect to the point of interest. The collected data is sent to a cloud server, which operates as storage center and in which the data can be latter accessed for subsequent analysis. The measurements are sent to the server by means of a wireless communication protocol, carried out by a General Packet Radio Service, GPRS, communication module connected to each station. In this way, the coverage of the network is not limited by issues such as the use of local area networks which at the same time facilitates the transportation and installation of the stations at any desired measurement site. Since each station can collect large amounts of data during a given period of time, it was necessary to implement techniques such as Big Data in order to extract important information and to identify patterns from the data such as the areas having the highest concentration of gases and possible correlations with other variables such as local weather conditions. This information could be used to support the making of decisions that benefit the communities impacted by air pollution, for example the early triggering of bad air quality alarms or the development of policies to regulate industry operation that can potentially impact the health of neighboring communities. A pilot case study was implemented in the city of Floridablanca, Colombia, to demonstrate the monitoring of the emissions of hydrogen sulfide of a big wastewater processing plant.

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Analyzing and Improving the Performance of a Particulate Matter Low Cost Air Quality Monitoring Device

Atmosphere ◽

10.3390/atmos12020251 ◽

2021 ◽

Vol 12 (2) ◽

pp. 251

Author(s):

Evangelos Bagkis ◽

Theodosios Kassandros ◽

Marinos Karteris ◽

Apostolos Karteris ◽

Kostas Karatzas

Keyword(s):

Air Pollution ◽

Particulate Matter ◽

Air Quality ◽

Urban Areas ◽

Low Cost ◽

Machine Learning Algorithms ◽

Quality Monitoring ◽

Reference Station ◽

Air Quality Monitoring ◽

Pollution Levels

Air quality (AQ) in urban areas is deteriorating, thus having negative effects on people’s everyday lives. Official air quality monitoring stations provide the most reliable information, but do not always depict air pollution levels at scales reflecting human activities. They also have a high cost and therefore are limited in number. This issue can be addressed by deploying low cost AQ monitoring devices (LCAQMD), though their measurements are of far lower quality. In this paper we study the correlation of air pollution levels reported by such a device and by a reference station for particulate matter, ozone and nitrogen dioxide in Thessaloniki, Greece. On this basis, a corrective factor is modeled via seven machine learning algorithms in order to improve the quality of measurements for the LCAQMD against reference stations, thus leading to its on-field computational improvement. We show that our computational intelligence approach can improve the performance of such a device for PM10 under operational conditions.

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Données LiDAR aériennes: pertinence de l'interprétation visuelle pour la foresterie

Schweizerische Zeitschrift fur Forstwesen ◽

10.3188/szf.2017.0127 ◽

2017 ◽

Vol 168 (3) ◽

pp. 127-133

Author(s):

Matthew Parkan

Keyword(s):

Cross Sections ◽

Forest Edge ◽

Airborne Lidar ◽

Visual Interpretation ◽

Individual Tree ◽

Small Areas ◽

Interactive 3D ◽

The Individual ◽

Airborne Lidar Data ◽

Stem Position

Airborne LiDAR data: relevance of visual interpretation for forestry Airborne LiDAR surveys are particularly well adapted to map, study and manage large forest extents. Products derived from this technology are increasingly used by managers to establish a general diagnosis of the condition of forests. Less common is the use of these products to conduct detailed analyses on small areas; for example creating detailed reference maps like inventories or timber marking to support field operations. In this context, the use of direct visual interpretation is interesting, because it is much easier to implement than automatic algorithms and allows a quick and reliable identification of zonal (e.g. forest edge, deciduous/persistent ratio), structural (stratification) and point (e.g. tree/stem position and height) features. This article examines three important points which determine the relevance of visual interpretation: acquisition parameters, interactive representation and identification of forest characteristics. It is shown that the use of thematic color maps within interactive 3D point cloud and/or cross-sections makes it possible to establish (for all strata) detailed and accurate maps of a parcel at the individual tree scale.

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Differentiation of endemic areas by incidence rates of tick-borne infectious diseases as the basis for choosing prevention strategy and tactics

ЗДОРОВЬЕ НАСЕЛЕНИЯ И СРЕДА ОБИТАНИЯ - ЗНиСО / PUBLIC HEALTH AND LIFE ENVIRONMENT ◽

10.35627/2219-5238/2019-321-12-56-61 ◽

2019 ◽

pp. 56-61

Author(s):

N.V. Rudakov ◽

N.A. Penyevskaya ◽

D.A. Saveliev ◽

S.A. Rudakova ◽

C.V. Shtrek ◽

...

Keyword(s):

High Risk ◽

Infectious Diseases ◽

Population Based ◽

Preventive Therapy ◽

Incidence Rates ◽

Prevention Strategy ◽

Individual Risk ◽

The Individual ◽

Integral Assessment ◽

Very High

Research objective. Differentiation of natural focal areas of Western Siberia by integral incidence rates of tick-borne infectious diseases for determination of the strategy and tactics of their comprehensive prevention. Materials and methods. A retrospective analysis of official statistics for the period 2002-2018 for eight sub-federal units in the context of administrative territories was carried out. The criteria of differentiation were determined by means of three evaluation scales, including long-term mean rates of tick-borne encephalitis, tick-borne borreliosis, and Siberian tick-borne typhus. As a scale gradation tool, we used the number of sample elements between the confidence boundaries of the median. The integral assessment was carried out by the sum of points corresponding to the incidence rates for each of the analyzed infections. Results. The areas of low, medium, above average, high and very high risk of tick-borne infectious diseases were determined. Recommendations on the choice of prevention strategy and tactics were given. In areas of very high and high incidence rates, a combination of population-based and individual prevention strategies is preferable while in other areas a combination of high-risk and individual strategies is recommended. Discussion. Epidemiologic zoning should be the basis of a risk-based approach to determining optimal volumes and directions of preventive measures against natural focal infections. It is necessary to improve the means and methods of determining the individual risk of getting infected and developing tick-borne infectious diseases in case of bites, in view of mixed infection of vectors, as well as methods of post-exposure disease prevention (preventive therapy).

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Pilot Operation of Vehicle Rating System in Seoul’s Green Transportation Zone and the Reduction of Particulate Matter and Air Pollution Levels

The Korean Association of Urban Policies ◽

10.21447/jup.2020.11.2.79 ◽

2020 ◽

Vol 11 (2) ◽

pp. 79-94

Author(s):

Sangjin Park ◽

Hyungkyoo Kim

Keyword(s):

Air Pollution ◽

Particulate Matter ◽

Rating System ◽

Pollution Levels ◽

Green Transportation

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The Need for Personalized Risk-Stratified Approaches to Treatment for Gestational Diabetes: A Narrative Review

Seminars in Reproductive Medicine ◽

10.1055/s-0041-1723778 ◽

2021 ◽

Author(s):

Shamil D. Cooray ◽

Jacqueline A. Boyle ◽

Georgia Soldatos ◽

Shakila Thangaratinam ◽

Helena J. Teede

Keyword(s):

Gestational Diabetes ◽

Pregnancy Outcomes ◽

Population Based ◽

Adverse Pregnancy Outcomes ◽

Therapeutic Interventions ◽

Clinical Prediction Model ◽

Increased Risk ◽

Adverse Pregnancy ◽

The Individual ◽

Personalized Risk

AbstractGestational diabetes mellitus (GDM) is common and is associated with an increased risk of adverse pregnancy outcomes. However, the prevailing one-size-fits-all approach that treats all women with GDM as having equivalent risk needs revision, given the clinical heterogeneity of GDM, the limitations of a population-based approach to risk, and the need to move beyond a glucocentric focus to address other intersecting risk factors. To address these challenges, we propose using a clinical prediction model for adverse pregnancy outcomes to guide risk-stratified approaches to treatment tailored to the individual needs of women with GDM. This will allow preventative and therapeutic interventions to be delivered to those who will maximally benefit, sparing expense, and harm for those at a lower risk.

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Comparing Charlson and Elixhauser comorbidity indices with different weightings to predict in-hospital mortality: an analysis of national inpatient data

BMC Health Services Research ◽

10.1186/s12913-020-05999-5 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Narayan Sharma ◽

René Schwendimann ◽

Olga Endrich ◽

Dietmar Ausserhofer ◽

Michael Simon

Keyword(s):

Hospital Mortality ◽

Patient Population ◽

Generalized Additive Models ◽

Routine Data ◽

Predictive Performance ◽

Population Based ◽

Mortality Prediction ◽

Additive Models ◽

General Hospitals ◽

Net Reclassification Improvement

Abstract Background Understanding how comorbidity measures contribute to patient mortality is essential both to describe patient health status and to adjust for risks and potential confounding. The Charlson and Elixhauser comorbidity indices are well-established for risk adjustment and mortality prediction. Still, a different set of comorbidity weights might improve the prediction of in-hospital mortality. The present study, therefore, aimed to derive a set of new Swiss Elixhauser comorbidity weightings, to validate and compare them against those of the Charlson and Elixhauser-based van Walraven weights in an adult in-patient population-based cohort of general hospitals. Methods Retrospective analysis was conducted with routine data of 102 Swiss general hospitals (2012–2017) for 6.09 million inpatient cases. To derive the Swiss weightings for the Elixhauser comorbidity index, we randomly halved the inpatient data and validated the results of part 1 alongside the established weighting systems in part 2, to predict in-hospital mortality. Charlson and van Walraven weights were applied to Charlson and Elixhauser comorbidity indices. Derivation and validation of weightings were conducted with generalized additive models adjusted for age, gender and hospital types. Results Overall, the Elixhauser indices, c-statistic with Swiss weights (0.867, 95% CI, 0.865–0.868) and van Walraven’s weights (0.863, 95% CI, 0.862–0.864) had substantial advantage over Charlson’s weights (0.850, 95% CI, 0.849–0.851) and in the derivation and validation groups. The net reclassification improvement of new Swiss weights improved the predictive performance by 1.6% on the Elixhauser-van Walraven and 4.9% on the Charlson weights. Conclusions All weightings confirmed previous results with the national dataset. The new Swiss weightings model improved slightly the prediction of in-hospital mortality in Swiss hospitals. The newly derive weights support patient population-based analysis of in-hospital mortality and seek country or specific cohort-based weightings.

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