Influence of Particle Composition and Size on the Accuracy of Low Cost PM Sensors: Findings From Field Campaigns

In the last decade, many low-cost monitoring sensors and sensor-networks have been used as an alternative air quality assessment method. It is also well known that these low cost monitors have calibration, accuracy and long term variation problems which require various calibration techniques. In this work PM2.5 and PM10 low cost sensors (Plantower and Nova Fitness) have been tested in five cities under different environmental conditions and compared with collocated standard instruments. Simultaneously, particle composition (organic and black carbon, sulfate, nitrate, chloride, ammonium, and chemical elements) has been measured in the same places to study its influence on the accuracy. The results show a very large variability in the correlation between the low cost sensors and collocated standard instruments depending on the composition and size of particles present in the site. The PM10 correlation coefficient (R2) between the low cost sensor and a collocated regulatory instrument varied from to 0.95 in Temuco to 0.04 in Los Caleos. PM2.5 correlation varied from 0.97 to 0.68 in the same places. It was found that sites that had higher proportion of large particles had lower correlation between the low cost sensor and the regulatory instrument. Sites that had higher relative concentration of organic and black carbon had better correlation because these species are mostly below the 1 μm size range. Sites that had higher sulfate, nitrate or SiO2 concentrations in PM2.5 or PM10 had low correlation most likely because these particles have a scattering coefficients that depends on its size or composition, thus they can be classified incorrectly.

Application of fingerprint combined with quantitative analysis and multivariate chemometric methods in quality evaluation of dandelion ( Taraxacum mongolicum )

A quality assessment method based on quantitative analysis of multi-components by single marker (QAMS) and fingerprint was constructed from 15 batches of dandelion ( Taraxacum mongolicum ), using multivariate chemometric methods (MCM). MCM were established by hierarchical cluster analysis (HCA) and factor analysis (FA). HCA was especially performed using the R language and SPSS 22.0 software. The relative correction factors of chlorogenic acid, caffeic acid, p-coumaric acid, luteolin and apigenin were calculated with cichoric acid as a reference, and their contents were determined. The differences between external standard method (ESM) and QAMS were compared. There was no significant difference ( t -test, p > 0.05) in quantitative determination, proving the consistency of the two methods (QAMS and ESM). Dandelion material from Yuncheng, Shandong was used as a reference chromatogram. The fingerprints in 15 batches of dandelion were established by HPLC analysis. The similarity of the fingerprints in different batches of dandelion material was greater than or equal to 0.82. A total of 10 common peaks were identified. This strategy is simple, rapid and efficient in multiple component detection of dandelion. It is beneficial in simplifying dandelion's quality control processes and providing references to enhance quality control for other herbal medicines.

A Comprehensive Method for Water Environment Assessment considering Trends of Water Quality

Water quality is a significant issue, and its assessment plays an important role in environmental management and pollution control. In this paper, we proposed a comprehensive water quality assessment method which takes into account both absolute and temporal trends in water quality. As the first step, we derived and applied a comprehensive pollution index (CPI) to characterize water pollution in 16 major tributaries to the Danjiangkou Reservoir, located in the upper reaches of the Hanjiang River in China. Next, we used Spearman’s rank correlation analysis to quantify temporal CPI trends in each tributary. As the final step, we conducted principal component analysis (PCA) using data on 8 water quality parameters and the temporal CPI trend from each of the 16 tributaries. The resultant comprehensive water quality assessment method identified tributaries, which stand to improve and threaten water quality in the Danjiangkou Reservoir from both immediate and future perspectives.

Comparison of Semirigorous and Empirical Models Derived Using Data Quality Assessment Methods

With the increase in available data and the stricter control requirements for mineral processes, the development of automated methods for data processing and model creation are becoming increasingly important. In this paper, the application of data quality assessment methods for the development of semirigorous and empirical models of a primary milling circuit in a platinum concentrator plant is investigated to determine their validity and how best to handle multivariate input data. The data set used consists of both routine operating data and planned step tests. Applying the data quality assessment method to this data set, it was seen that selecting the appropriate subset of variables for multivariate assessment was difficult. However, it was shown that it was possible to identify regions of sufficient value for modeling. Using the identified data, it was possible to fit empirical linear models and a semirigorous nonlinear model. As expected, models obtained from the routine operating data were, in general, worse than those obtained from the planned step tests. However, using the models obtained from routine operating data as the initial seed models for the automated advanced process control methods would be extremely helpful. Therefore, it can be concluded that the data quality assessment method was able to extract and identify regions sufficient and acceptable for modeling.

Assessing the Urban Eco-Environmental Quality by the Remote-Sensing Ecological Index: Application to Tianjin, North China

The remote-sensing ecological index (RSEI), which is built with greenness, moisture, dryness, and heat, has become increasingly recognized for its use in urban eco-environment quality assessment. To improve the reliability of such assessment, we propose a new RSEI-based urban eco-environment quality assessment method where the impact of RSEI indicators on the eco-environment quality and the seasonal change of RSEI are examined and considered. The northern Chinese municipal city of Tianjin was selected as a case study to test the proposed method. Landsat images acquired in spring, summer, autumn, and winter were obtained and processed for three different years (1992, 2005, and 2018) for a multitemporal analysis. Results from the case study show that both the contributions of RSEI indicators to eco-environment quality and RSEI values vary with the season and that such seasonal variability should be considered by normalizing indicator measures differently and using more representative remote-sensing images, respectively. The assessed eco-environment quality of Tianjin was, overall, improving owing to governmental environmental protection measures, but the damage caused by rapid urban expansion and sea reclamation in the Binhai New Area still needs to be noted. It is concluded that our proposed urban eco-environment quality assessment method is viable and can provide a reliable assessment result that helps gain a more accurate understanding of the evolution of the urban eco-environment quality over seasons and years.