scholarly journals Evaluation of field calibration methods and performance of AQMesh, a low-cost air quality monitor

2021 ◽  
Vol 193 (5) ◽  
Author(s):  
Dan Wahlborg ◽  
Mikael Björling ◽  
Magnus Mattsson
Keyword(s):  
Air Quality ◽  
Environmental Quality ◽  
Reference Data ◽  
Multivariate Calibration ◽  
Limit Of Detection ◽  
Reference Station ◽  
Raw Data ◽  
Low Concentrations ◽  
Calibration Methods ◽  
Data Points

AbstractField calibrations of NO2, NO, and PM10 from AQMesh Air Quality Monitors (AQMs) were conducted during a summer and an autumn period in a busy street in a midsize Swedish city. All the three linear calibration procedures studied (postscaled, bisquare, and orthogonal data) significantly reduced the ranges and magnitudes of the performance indicators to yield more reliable results than the raw data. The improvements were sufficient to satisfy the European Union (EU) Data Quality Objective (DQO) for indicative measurements as compared to reference data only for NO2 (above 50 µg m−3) and NO (above 30 µg m−3) during the autumn calibration period. The relatively simple bisquare procedure had the best performance overall. The bisquare procedure improved the root mean square error by the same amount as other studies using complex multivariate calibration methods. Low concentrations of pollutants were measured, far below the EU Environmental Quality Standard thresholds and even satisfying the future goals for the Environmental Quality Objectives. Cleaning the raw data by removing data points in the reference data that were below the reference station limit of detections (and the synchronous data points in the AQM prescaled data) was found to improve the performances of the calibration procedures appreciably. Many NO2 and almost all PM10 data points in this study fell below the AQM limit of detection. These low concentrations will probably be a common problem in many field studies, at least in areas with relatively low air pollution. However, the relative errors were sufficiently low for these data points that they could be interpreted as accurately representing low concentrations and did not need to be removed from the datasets. For the NO2 measurements, a slight periodic error correlated with sunlight and increased ambient temperature was noted. NO measurements correlated strongly with increased traffic.

scholarly journals Simultaneous spectrophotometric determination of phenanthridine, phenanthridinone and phenanthridine N-oxide using multivariate calibration methods

2010 ◽  
Vol 8 (1) ◽  
pp. 207-213 ◽  
Author(s):  
Mohammad-Hossein Sorouraddin ◽  
Kaveh Amini ◽  
Abdolhossein Naseri ◽  
Mohammad-Reza Rashidi
Keyword(s):  
Human Plasma ◽  
Multivariate Calibration ◽  
Limit Of Detection ◽  
Ternary Mixtures ◽  
Plasma Samples ◽  
Spiked Human Plasma ◽  
Human Plasma Samples ◽  
Calibration Methods ◽  
Pre Treatment

AbstractThe multivariate calibration methods, partial least squares (PLS) and principle component regression (PCR) have been used to determine phenanthridine, phenanthridinone and phenanthridine N-oxide in spiked human plasma samples. Resolution of binary and ternary mixtures of analytes with minimum sample pre-treatment and without analyte separation has been successfully achieved analyzing the UV spectral data. The net analyte signal (NAS) concept was also used to calculate multivariate analytical figures of merit such as limit of detection, selectivity and sensitivity. The simultaneous determination of three analytes was possible by PLS and PCR processing of sample absorbance in the 210–355 nm region. Good recoveries were obtained for both synthetic mixtures and spiked human plasma samples.

CAMS-Net: The Clean Air Monitoring and Solutions Network

2021 ◽  
Author(s):  
Daniel Westervelt ◽  
Celeste McFarlane ◽  
Faye McNeill ◽  
R (Subu) Subramanian ◽  
Mike Giordano ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Low Cost ◽  
Air Monitoring ◽  
Quality Data ◽  
Calibration Methods ◽  
The World ◽  
Clean Air

<p>There is a severe lack of air pollution data around the world. This includes large portions of low- and middle-income countries (LMICs), as well as rural areas of wealthier nations as monitors tend to be located in large metropolises. Low cost sensors (LCS) for measuring air pollution and identifying sources offer a possible path forward to remedy the lack of data, though significant knowledge gaps and caveats remain regarding the accurate application and interpretation of such devices.</p><p>The Clean Air Monitoring and Solutions Network (CAMS-Net) establishes an international network of networks that unites scientists, decision-makers, city administrators, citizen groups, the private sector, and other local stakeholders in co-developing new methods and best practices for real-time air quality data collection, data sharing, and solutions for air quality improvements. CAMS-Net brings together at least 32 multidisciplinary member networks from North America, Europe, Africa, and India. The project establishes a mechanism for international collaboration, builds technical capacity, shares knowledge, and trains the next generation of air quality practitioners and advocates, including domestic and international graduate students and postdoctoral researchers. </p><p>Here we present some preliminary research accelerated through the CAMS-Net project. Specifically, we present LCS calibration methodology for several co-locations in LMICs (Accra, Ghana; Kampala, Uganda; Nairobi, Kenya; Addis Ababa, Ethiopia; and Kolkata, India), in which reference BAM-1020 PM2.5 monitors were placed side-by-side with LCS. We demonstrate that both simple multiple linear regression calibration methods for bias-correcting LCS and more complex machine learning methods can reduce bias in LCS to close to zero, while increasing correlation. For example, in Kampala, Raw PurpleAir PM2.5 data are strongly correlated with the BAM-1020 PM2.5 (r<sup>2</sup> = 0.88), but have a mean bias of approximately 12 μg m<sup>-3</sup>. Two calibration models, multiple linear regression and a random forest approach, decrease mean bias from 12 μg m<sup>-3 </sup>to -1.84 µg m<sup>-3</sup> or less and improve the the r<sup>2</sup> from 0.88 to 0.96. We find similar performance in several other regions of the world. Location-specific calibration of low-cost sensors is necessary in order to obtain useful data, since sensor performance is closely tied to environmental conditions such as relative humidity. This work is a first step towards developing a database of region-specific correction factors for low cost sensors, which are exploding in popularity globally and have the potential to close the air pollution data gap especially in resource-limited countries. </p><p> </p><p> </p>

scholarly journals Examining the spatial relationship between environmental health factors and house prices

2018 ◽  
Vol 11 (3) ◽  
pp. 353-398 ◽  
Author(s):  
Michael J. McCord ◽  
Sean MacIntyre ◽  
Paul Bidanset ◽  
Daniel Lo ◽  
Peadar Davis
Keyword(s):  
Air Quality ◽  
Environmental Quality ◽  
House Prices ◽  
Management Strategies ◽  
Noise Pollution ◽  
Well Being ◽  
Spatial Modelling ◽  
Content Type ◽  
The Impact ◽  
Health And Well Being

Purpose Air quality, noise and proximity to urban infrastructure can arguably have an important impact on the quality of life. Environmental quality (the price of good health) has become a central tenet for consumer choice in urban locales when deciding on a residential neighbourhood. Unlike the market for most tangible goods, the market for environmental quality does not yield an observable per unit price effect. As no explicit price exists for a unit of environmental quality, this paper aims to use the housing market to derive its implicit price and test whether these constituent elements of health and well-being are indeed capitalised into property prices and thus implicitly priced in the market place. Design/methodology/approach A considerable number of studies have used hedonic pricing models by incorporating spatial effects to assess the impact of air quality, noise and proximity to noise pollutants on property market pricing. This study presents a spatial analysis of air quality and noise pollution and their association with house prices, using 2,501 sale transactions for the period 2013. To assess the impact of the pollutants, three different spatial modelling approaches are used, namely, ordinary least squares using spatial dummies, a geographically weighted regression (GWR) and a spatial lag model (SLM). Findings The findings suggest that air quality pollutants have an adverse impact on house prices, which fluctuate across the urban area. The analysis suggests that the noise level does matter, although this varies significantly over the urban setting and varies by source. Originality/value Air quality and environmental noise pollution are important concerns for health and well-being. Noise impact seems to depend not only on the noise intensity to which dwellings are exposed but also on the nature of the noise source. This may suggest the presence of other externalities that arouse social aversion. This research presents an original study utilising advanced spatial modelling approaches. The research has value in further understanding the market impact of environmental factors and in providing findings to support local air zone management strategies, noise abatement and management strategies and is of value to the wider urban planning and public health disciplines.

scholarly journals Prediction of Total Phenolic Content in Extracts of Prunella Species from HPLC Profiles by Multivariate Calibration

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Saliha Sahin ◽  
Esra Isik ◽  
Cevdet Demir
Keyword(s):  
High Performance ◽  
Multivariate Calibration ◽  
Principal Component Regression ◽  
Principal Component ◽  
Total Phenol ◽  
Total Phenolic ◽  
Total Phenol Content ◽  
Phenol Content ◽  
Calibration Methods ◽  
Hplc Profiles

The multivariate calibration methods—principal component regression (PCR) and partial least squares (PLSs)—were employed for the prediction of total phenol contents of four Prunella species. High performance liquid chromatography (HPLC) and spectrophotometric approaches were used to determine the total phenol content of the Prunella samples. Several preprocessing techniques such as smoothing, normalization, and column centering were employed to extract the chemically relevant information from the data after alignment with correlation optimized warping (COW). The importance of the preprocessing was investigated by calculating the root mean square error (RMSE) for the calibration set of the total phenol content of Prunella samples. The models developed based on the preprocessed data were able to predict the total phenol content with a precision comparable to that of the reference of the Folin-Ciocalteu method. PLS model seems preferable, because of its predictive and describing abilities and good interpretability of the contribution of compounds to the total phenol content. Multivariate calibration methods were constructed to model the total phenol content of the Prunella samples from the HPLC profiles and indicate peaks responsible for the total phenol content successfully.

scholarly journals Classification and Authentication of Paprika by UHPLC-HRMS Fingerprinting and Multivariate Calibration Methods (PCA and PLS-DA)

Foods ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 486 ◽  
Author(s):  
Sergio Barbosa ◽  
Javier Saurina ◽  
Lluís Puignou ◽  
Oscar Núñez
Keyword(s):  
Multivariate Calibration ◽  
Principal Component ◽  
Small Region ◽  
Reversed Phase ◽  
Negative Ion ◽  
Mass Analyzer ◽  
Least Squares Regression ◽  
Calibration Methods ◽  
Chemical Descriptors ◽  
Negative Ion Mode

In this study, the feasibility of non-targeted UHPLC-HRMS fingerprints as chemical descriptors to address the classification and authentication of paprika samples was evaluated. Non-targeted UHPLC-HRMS fingerprints were obtained after a simple sample extraction method and C18 reversed-phase separation. Fingerprinting data based on signal intensities as a function of m/z values and retention times were registered in negative ion mode using a q-Orbitrap high-resolution mass analyzer, and the obtained non-targeted UHPLC-HRMS fingerprints subjected to unsupervised principal component analysis (PCA) and supervised partial least squares regression-discriminant analysis (PLS-DA) to study sample discrimination and classification. A total of 105 paprika samples produced in three different regions, La Vera PDO and Murcia PDO, in Spain, and the Czech Republic, and all of them composed of samples of at least two different taste varieties, were analyzed. Non-targeted UHPLC-HRMS fingerprints demonstrated to be excellent sample chemical descriptors to achieve the authentication of paprika production regions with 100% sample classification rates by PLS-DA. Besides, the obtained fingerprints were also able to perfectly discriminate among the different paprika taste varieties in all the studied cases, even in the case of the different La Vera PDO paprika tastes (sweet, bittersweet, and spicy) which are produced in a very small region.

scholarly journals Stability-Indicating Photochemical Method for the Assay of Thiamine by Spectrophotometry

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Iqbal Ahmad ◽  
Syed Haider Abbas ◽  
Zubair Anwar ◽  
Muhammad Ali Sheraz ◽  
Sofia Ahmed ◽  
...  
Keyword(s):  
Limit Of Detection ◽  
The United States ◽  
Limit Of Quantification ◽  
Chromatography Method ◽  
Stability Indicating ◽  
Low Concentrations ◽  
Photochemical Method ◽  
Significant Difference ◽  
Liquid Chromatography Method ◽  
States Pharmacopeia

A stability-indicating photochemical method has been developed for the assay of thiamine (TH) salts in aqueous solution and in fresh and aged vitamin preparations. It is based on the photooxidation of TH by UV irradiation to form thiochrome (TC) in alkaline solution. The TC : TH ratio under controlled conditions of light intensity, temperature, pH, exposure time, and irradiation distance is constant and can be used to determine the concentration of UV irradiated TH solutions. TC, on extraction with isobutanol from the photodegraded solution of TH, has been determined by the UV spectrophotometric method at 370 nm. It exhibits a high intensity of absorption in the UV region that can be used for the assay of even low concentrations of TH. Under optimum conditions, Beer’s law is obeyed in the concentration range of 0.20–2.00 mg/100 ml (R2 = 09998). The limit of detection (LOD) and limit of quantification (LOQ) are 0.0076 and 0.0231 mg/100 ml, respectively. The method has been validated and applied to aqueous solutions and vitamin preparations. The results have statistically been compared with the United States Pharmacopeia liquid chromatography method. It has been found that there is no significant difference between the two methods at 95% confidence level.

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