Influence of relative humidity on real-time measurements of particulate matter concentration via light scattering

2020 ◽  
Vol 139 ◽  
pp. 105462 ◽  
Author(s):  
Jinke Han ◽  
Xiaowei Liu ◽  
Dong Chen ◽  
Meng Jiang
Micromachines ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 416
Author(s):  
Sama Molaie ◽  
Paolo Lino

Due to the adverse effects on human health and the environment, air quality monitoring, specifically particulate matter (PM), has received increased attention over the last decades. Most of the research and policy actions have been focused on decreasing PM pollution and the development of air monitoring technologies, resulting in a decline of total ambient PM concentrations. For these reasons, there is a continually increasing interest in mobile, low-cost, and real-time PM detection instruments in both indoor and outdoor environments. However, to the best of the authors’ knowledge, there is no recent literature review on the development of newly designed mobile and compact optical PM sensors. With this aim, this paper gives an overview of the most recent advances in mobile optical particle counters (OPCs) and camera-based optical devices to detect particulate matter concentration. Firstly, the paper summarizes the particulate matter effects on human health and the environment and introduces the major particulate matter classes, sources, and characteristics. Then, it illustrates the different theories, detection methods, and operating principles of the newly developed portable optical sensors based on light scattering (OPCs) and image processing (camera-based sensors), including their advantages and disadvantages. A discussion concludes the review by comparing different novel optical devices in terms of structures, parameters, and detection sensitivity.


Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2243
Author(s):  
Dong Chen ◽  
Xiaowei Liu ◽  
Jinke Han ◽  
Meng Jiang ◽  
Zhaofeng Wang ◽  
...  

Under the condition of ultra-low emission for power plants, the particulate matter concentration is significantly lower than that of typical power plants a decade ago, which posed new challenges for the particulate matter monitoring of stationary emission. The monitoring of particulate matter mass concentration based on ensemble light scattering has been found affected by particle size. Thus, this study develops a method of using the scattering angular distribution to obtain the real-time particle size, and then correct the particulate matter concentration with the real-time measured particle size. In this study, a real-time aerosol concentration and particle size measurement setup is constructed with a fixed detector at the forward direction and a rotating detector. The mass concentration is measured by the fixed detector, and the particle size is measured from the intensity ratio of the two detectors. The simulations show that the particle size has power law functionality with the angular spacing of the ripple structure according to Mie theory. Four quartz aerosols with different particle size are tested during the experiment, and the particle size measured from the ripple width is compared with the mass median size measured by an electrical low pressure impactor (ELPI). Both techniques have the same measurement tendency, and the measurement deviation by the ripple width method compared with ELPI is less than 15%. Finally, the measurement error of the real-time mass concentration is reduced from 38% to 18% with correction of the simultaneously measured particle size when particle size has changed.


2020 ◽  
Vol 1 (2) ◽  
pp. 107-113
Author(s):  
N.V. Krishna Prasad ◽  
M.S.S.R.K.N. Sarma ◽  
P. Sasikala ◽  
Naga Raju M ◽  
N. Madhavi

Particulate matter concentration and its study has gained tremendous significance in view of increase in air pollution. Since air pollution has many adverse effects on mankind, measures may be taken by observing the trends in PM2.5 (particulate matter) and concentrations of pollutants like NO2, SO2, NO2, NO, NOx, CO, NH3 and RH(Relative Humidity)  as well as temperature. Even though continuous monitoring of air pollution in urban locations has been increasing in view of its huge impact on the sustainable development and ecological balance a regression model is essential always to analyse large sets of data. These regression models also play vital role in some cases where data was not observed due to unavoidable circumstances and during times when the measuring instruments do not work. In this context an attempt was made to develop a regression model exclusively for Visakhapatnam(India) a coastal, urban and industrial station and to analyse the trends in particulate matter concentration at this staion. A regression model was developed with PM2.5 as dependent variable and SO2, NOx, NO2, CO, NH3, temperature(Temp) and relative humidity(RH) as independent variables. The efficiency of the model was tested with known independent variables and PM2.5 was estimated. It is found that observed and estimated PM2.5 values are highly correlated.


Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 580
Author(s):  
Eyal Fattal ◽  
Hadas David-Saroussi ◽  
Ziv Klausner ◽  
Omri Buchman

The accumulated particulate matter concentration at a given vertical column due to traffic sources in urban area has many important consequences. This task, however, imposes a major challenge, since the problem of realistic pollutant dispersion in an urban environment is a very demanding task, both theoretically and computationally. This is mainly due to the highly inhomogeneous three dimensional turbulent flow regime in the urban canopy roughness sublayer, which is far from “local equilibrium” between shear production and dissipation. We present here a mass-consistent urban Lagrangian stochastic model for pollutants dispersion, where the flow field is modeled using a hybrid approach by which we model the surface layer based on the typical turbulent scales, both of the canopy and in the surface layer inertial sub-layer. In particular it relies on representing the canopy aerodynamically as a porous medium by spatial averaging the equations of motion, with the assumption that the canopy is laterally uniform on a scale much larger than the buildings but smaller than the urban block/neighbourhood, i.e., at the sub-urban-block scale. Choosing the spatial representative averaging volume allows the averaged variables to reflect the characteristic vertical heterogeneity of the canopy but to smooth out smaller scale spatial fluctuations caused as air flows in between the buildings. This modeling approach serves as the base for a realistic and efficient methodology for the calculation of the accumulated concentration from multiple traffic sources for any vertical column in the urban area. The existence of multiple traffic sources impose further difficulty since the computational effort required is very demanding for practical uses. Therefore, footprint analysis screening was introduced to identify the relevant part of the urban area which contributes to the chosen column. All the traffic sources in this footprint area where merged into several areal sources, further used for the evaluation of the concentration profile. This methodology was implemented for four cases in the Tel Aviv metropolitan area based on several selected summer climatological scenarios. We present different typical behaviors, demonstrating combination of source structure, urban morphology, flow characteristics, and the resultant dispersion pattern in each case.


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