Dimitra Karali, Glykeria Loupa  , Spyridon Rapsomanikis 

Laboratory of Atmospheric Pollution and Pollution Control Engineering of Atmospheric Pollutants, Department of Environmental Engineering, Faculty of Engineering, Democritus University of Thrace, 67100 Xanthi, Greece


Received: April 20, 2020
Revised: October 2, 2020
Accepted: November 3, 2020

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.4209/aaqr.2020.04.0159  

Cite this article:

Karali, D., Loupa, G., Rapsomanikis, S. (2021). Nephelometer Sensitivities for the Determination of PM2.5 Mass Concentration in Ambient and Indoor Air. Aerosol Air Qual. Res. 21, 200159. https://doi.org/10.4209/aaqr.2020.04.0159


  • On line PM2.5 mass concentration monitoring with nephelometry.
  • Light scattering coefficient dependence on relative humidity.
  • Indoor aerosol mass concentrations and densities.


Simple algorithms are presented for the relationships between the gravimetrically measured PM2.5 mass concentration and a nephelometer particle scattering coefficient, for three different environments: two outdoor locations (an urban and a suburban) and one indoors. With these algorithms, the aerosol light scattering coefficients of the nephelometer (provided on line with a time step of seconds) can be related to PM2.5 mass concentrations. The effect of the relative humidity on the nephelometer readings was also evaluated with two models. In the last two campaigns (in the suburbs of the city and in a laboratory), a drying device before the aerosol entrance to the nephelometer was used, a Nafion™ dryer. In the indoor environment, the two methods (gravimetry and nephelometry) were compared with the readings of an aerosol light scattering spectrometer, which provided PM2.5 volume concentrations and thus it was possible to calculate the PM2.5 density indoors.

Keywords: PM2.5 gravimetric mass concentrations, Aerosol light scattering coefficient, Nephelometric measurements, Indoor aerosol density


Indoor and outdoor aerosol present a significant risk factor for the human health, as it has been asserted in numerous studies (Cohen et al., 2017; Nagel et al., 2018; Vardoulakis et al., 2019). The main effort nowadays is to reduce the atmospheric concentrations of the PM2.5 (particulate matter with aerodynamic diameter < 2.5 µm), because it is deleterious to health and is associated with respiratory and cardiopulmonary diseases, as well as lung cancer (Kim et al., 2015; Feng et al., 2016; Melki et al., 2017). Such an effort is mirrored, for example, in the European project “Urban PM2.5 Atlas - Air Quality in European cities”, which provides information on the levels of air pollution (specifically PM2.5) in 150 European cities (Thunis et al., 2017). The determination of the atmospheric concentrations of PM2.5 is regulated by national and international legislation and their monitoring is an imperative necessity.

Monitoring the atmospheric PM2.5 can be an expensive, time consuming and/or tedious task. Gravimetric methods are the standard reference methods (EU, 1999; EN, 2005). However, proxy methods that need calibration have been used by scientific groups worldwide, because of the ease of use, low purchase price and more importantly their continuous operation (recording time step of minutes) (Holstius et al., 2014; Budde et al., 2018). Such instrumentation may be based on the principle of β radiation attenuation by the collected aerosol, change in the frequency of oscillation in tapered oscillating microbalance, or the intensity of the light that is scattered by the aerosol flowing through an optical cell (Huang, 2007; Allen et al., 2011; Kulkarni et al., 2011; Kim, 2015). Comparison of these techniques and instruments, against each other and against international and European standard norms, is crucial to facilitate the PM2.5 monitoring (Waggoner and Weiss, 1980; Sioutas et al., 2000; Liu et al., 2002; Allen et al., 2003; Chow et al., 2006; Grimm and Eatough, 2009; Dinoi et al., 2017). In the present study, relationships between the gravimetrically measured PM2.5 mass concentration by an impactor and the PM2.5 light scattering coefficient as recorded by a nephelometer, were derived for three different environments: two outdoor locations (an urban and a suburban) and one indoors. The results will provide a simple and cost-effective method to estimate the PM2.5 mass concentrations on line (with a time step of minutes), albeit significant only for the above named, geographically defined locations. The effect of the relative humidity on the nephelometer readings was also evaluated (Liu et al., 2002; Chow et al., 2006) and it was the trigger to introduce an almost constant RH stream into the nephelometer. Furthermore, in the indoor environment, the PM2.5 mass concentrations (measured gravimetrically and estimated by the nephelometer data) were compared with the readings of another light scattering instrument (an aerosol light scattering spectrometer). This spectrometer provided also the PM2.5 volume concentrations that permitted the calculation of the PM2.5 density. The instrumental set up follows the philosophy of minimum cost and intervention.


The relationship between the mass concentration of PM2.5 and the aerosol light scattering coefficient of PM2.5 (Bsp) were examined during three monitoring campaigns in the city of Xanthi, Greece. The first period of measurements was conducted in the city centre of Xanthi, Greece (September 2013 and November 2014; referred to as “urban”). A Radiance Research M903 nephelometer (hereafter referred to as RRN-903; Radiance Research, Seattle, WA, USA), without drying the aerosol laden stream, was operated in parallel with a high volume PM2.5 sampler (TE-6001-2.5-I, Tisch Environmental Inc., Village of Cleves, OH, USA). The PM2.5 mass concentrations and the Bsp were monitored at the adult breathing height (∼1.7 m), adjacent to the pavement of a busy road. Temperature and relative humidity (RH) were also recorded (Vaisala HUMICAP®, Helsinki, Finland). During this campaign the effect of RH on the nephelometer readings was established. During June and July 2014 the nephelometer recorded data every 5 min in the centre of the city (the high volume sampler was not used during this period). From these data, eleven days had RH ≤ 40% and were used in the ANOVA presented in the results, for the case of the urban site.

During 2019, the sampling station was installed in the University campus, in the suburbs of Xanthi. Initially, the sampling was conducted outdoors (referred to as “suburban”) and finally inside a laboratory (referred to as “indoors”). During, these two campaigns the nephelometer operated with a Nafion™ dryer in place (see Fig. S1, in the supplementary material). The whole length of ½” I.D. tubing together with the length of the Nafion™ dryer was 2.5 meters, as determined by the algorithm of von der Weiden et al. (2009). The truncation error for PM2.5 is small, below 1.06 and for our data treatment was ignored (Anderson and Ogren, 1998; Müller et al., 2009).

Additionally, particle number, volume and mass concentrations were monitored inside the laboratory with a PALAS Promo 2000 light scattering spectrometer (which can classify particles in 40 size bins between 200–2500 nm, PALAS GmbH, Greschbachstraße 3b, 76229 Karlsruhe, Germany). Aerosol mass concentrations provided by the PALAS Promo 2000 are calculated by the software of the instrument with a default PM2.5 density of 1 g cm3. The 24 h average mass concentrations, acquired gravimetrically, along with the PM2.5 readings by the PALAS Promo, provided the opportunity to calculate the indoor PM2.5 densities. Also, the correlation of the readings of the two collocated light scattering instruments was examined.

Details for the monitoring methods can be found in the supplementary material.


3.1 The Bsp – RH Relationship

The relationship of the Bsp and the atmospheric water content, as expressed with the RH, was examined for the experimental data acquired during the first campaign, i.e., during 2013 and 2014. In Fig.1 the measured Bsp values are plotted against RH. Two models were applied: a simple linear model and an exponential model, presented in Table 1, along with the estimated fitting parameters of each model. The best fit model in our data was the linear model, probably due to the low RH levels (most of the time below 65% during this first campaign) and due to the small number of samples. The Bsp dependence on the RH was not unexpected and it has been extensively reported in the literature with several proposed algorithms to account for this dependence (Vincent, 2007; Zieger et al., 2013; Tryner et al., 2019). However, these relationships cannot be universal solutions, because atmospheric aerosol composition (and water affinity) varies with source, sampling point, season, day and minute. 

Fig. 1. The Bsp values dependency on RH (urban site, ambient RH).Fig. 1. The Bsp values dependency on RH (urban site, ambient RH). 

Table 1. The estimated parameters for two models applied for the relationship of Bsp (m–1) with RH (%).

During the other two campaigns in our study, the aerosol laden air stream was dried by a Nafion™ drier in the inlet of the nephelometer to a RH less than 40% for a flow of 2 L min–1. The flow rate and the size of the tubing (diameter and length) were calculated using the algorithms and the graphical user interface given by von der Weiden et al. (2009).

3.2 The Bsp Dependence on the Location of the Monitoring Station

The dependence of the Bsp values on the location of the nephelometer was evaluated through an Analysis of Variance (one-way ANOVA), which tested for significant differences between the means of the Bsp values for one categorical predictor (urban, suburban and indoors). For this analysis, the data from the urban environment (uncontrolled RH) that corresponded to days with a 24-h average RH ≤ 40% were used. The results of the ANOVA are statistically significant and corroborate that the nephelometer readings depended on the location of the monitoring station, (F = 6.38488 and p < 0.005194).

3.3 The Relationship between the Bsp and the Mass Concentrations of PM2.5

In Fig. 2 the average Bsp values were plotted against the respective average, gravimetric mass concentrations of PM2.5 for each sampling location, i.e., urban, suburban and indoors. The coefficient of determination (R2) for each linear model fitting was above 0.70, pointing out to a satisfactory relationship between the two variables. The slope of the regression line varied between 1.6 m2 g–1 to 4.5 m2 g–1, values that are similar to those reported in other studies (Liu et al., 2002; Chow et al., 2006; Hand and Malm, 2007). 

Fig. 2. The relationship Bsp-PM2.5 (gravimetrically measured).Fig. 2. The relationship Bsp-PM2.5 (gravimetrically measured).

Also, the PM2.5 mass concentrations acquired with the impactors can be used to convert nephelometer readings, in each case of the present study, into mass concentration in order to observe the PM2.5 variation in a time step of few minutes. These equations are:

PM2.5 (µg m–3) =0.55 × Bsp (Mm–1) + 1.13 (urban, ambient RH)                                                             (1)

PM2.5 (µg m–3) =0.42 × Bsp (Mm–1) + 2.47 (suburban, RH ≤ 40)                                                             (2)

The respective equation for the indoor laboratory environment is presented below, in Fig. 3

Fig. 3. The indoor relationship Bsp with PM2.5 mass concentrations, as measured gravimetrically and as calculated with the PALAS Promo, corrected for particle density.Fig. 3. The indoor relationship Bsp with PM2.5 mass concentrations, as measured gravimetrically and as calculated with the PALAS Promo, corrected for particle density.

3.3 Indoor PM2.5 Densities

In the laboratory, the two light scattering instruments were run simultaneously with the impactor. Note that the laboratory was void of people during the experiment, except the instrument operator, hence there were not PM emission events, which could disturb the readings of the nephelometer (Liu et al., 2002).

This experiment provided the opportunity to calculate PM2.5 densities. Assuming that the average PM2.5 mass concentration obtained indoors, gravimetrically, is the same concentration that the PALAS Promo detects, then the measured mass of PM2.5 divided by the total volume V of PM2.5 particles recorded by the PALAS Promo, will result in indoor aerosol densities, the last column in Table 2.

The calculated PM2.5 densities were used to correct the PALAS Promo PM2.5 mass concentrations (calculated by the instrument with the default density of 1 g cm–3). The PM2.5 mass concentrations (second column in the Table 2) were corrected by multiplying each value with the respective density in the last column of the Table 2. As can be seen from Table 2, the calculated indoor particle densities differed (mean = 0.95 (SD = 0.17) g cm–3) but not significantly from the arbitrary selected density of 1 g cm–3, due to absence of indoor emissions and a relatively unpolluted outdoor air in this case. The results indicate that arbitrarily selected densities of indoor aerosol for the calculation of their indoor concentrations may be erroneous, especially if an indoor environment has enhanced human activity, i.e., variable indoor sources. 

Table 2. Indoor PM2.5 densities.

The corrected PM2.5 mass concentrations of the PALAS Promo were better related with the gravimetrically derived values. The Relative Standard Error was 3.15% for the uncorrected and 2.46% for the corrected values. In Fig. 3, the corrected PM2.5 mass concentrations from the PALAS Promo and the PM2.5 mass concentrations from the gravimetric method are plotted against the respective the Bsp values to highlight the good correlation of the three methods.


The mass concentration of PM2.5 in a small time step is needed to trace aerosol variations. In the present study the PM2.5 mass concentrations were determined gravimetrically and related to the aerosol light scattering coefficient obtained by a RRN-903, during three campaigns at three different environments, i.e., an urban, a suburban and indoors. The data from the first campaign, in the city center, confirmed, as expected, that the aerosol light scattering coefficient was sensitive to the ambient RH. Two models were tested in order to express this relationship and a simple linear model has been proved to provide the best fit in this case. Thus, in the following two campaigns (in the suburbs of the city and in a laboratory), a drying device before the aerosol entrance to the nephelometer was used. For these campaigns, relationships between Bsp and the PM2.5 mass concentrations (gravimetric) were also derived. The presented algorithms can be applied in order to convert light scattering data to bulk PM2.5 mass concentration, using the nephelometer RRN-903 as a PM2.5 mass detector. However, as confirmed also by an Analysis of Variance, the Bsp is sensitive to the location of the monitoring station and there is a need for in situ calibration in each case. Furthermore, the densities of indoor particles with the same experimental set up with the cooperation of an aerosol spectrometer (PALAS Promo), which readings were well related with the nephelometer RRN-903, indicate their variability even in a supposedly controlled environment.


The present work was funded by Democritus University of Thrace (Greece) funds.


Allen, G.A., Miller, P.J., Rector, L.J., Brauer, M., Su, J.G. (2011). Characterization of valley winter woodsmoke concentrations in northern ny using highly time-resolved measurements. Aerosol Air Qual. Res. 11, 519–530. https://doi.org/10.4209/aaqr.2011.03.0031

Allen, R., Larson, T., Sheppard, L., Wallace, L., Liu, L.J.S. (2003). Use of real-time light scattering data to estimate the contribution of infiltrated and indoor-generated particles to indoor air. Environ. Sci. Technol. 37, 3484–3492. https://doi.org/10.1021/es021007e

Anderson, T.L., Ogren, J.A. (1998). Determining aerosol radiative properties using the tsi 3563 integrating nephelometer. Aerosol Sci. Technol. 29, 57–69. https://doi.org/10.1080/02786829808965551

Budde, M., Schwarz, A.D., Müller, T., Laquai, B., Streibl, N., Schindler, G., Köpke, M., Riedel, T., Dittler, A., Beigl, M. (2018). Potential and limitations of the low-cost SDS011 particle sensor for monitoring urban air quality. ProScience 5, 6–12. https://doi.org/10.14644/dust.2018.002

Chow, J.C., Watson, J.G., Park, K., Robinson, N.F., Lowenthal, D.H., Magliano, K.A. (2006). Comparison of particle light scattering and fine particulate matter mass in central California. J. Air Waste Manage. Assoc. 56, 398–410. https://doi.org/10.1080/10473289.2006.10464515

Cohen, A.J., Brauer, M., Burnett, R., Anderson, H.R., Frostad, J., Estep, K., Balakrishnan, K., Brunekreef, B., Dandona, L., Dandona, R., Feigin, V., Freedman, G., Hubbell, B., Jobling, L., Kan, H., Knibbs, L., Liu, Y., Martin, R., Morawska, L., … Forouzanfar, M.H. (2017). Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: An analysis of data from the global burden of diseases study 2015. Lancet 389, 1907–1918. https://doi.org/10.1016/S0140-6736(17)30505-6

Dinoi, A., Donateo, A., Belosi, F., Conte, M., Contini, D. (2017). Comparison of atmospheric particle concentration measurements using different optical detectors: Potentiality and limits for air quality applications. Measurement 106, 274–282. https://doi.org/10.1016/j.measurement.2016.02.019

Council of the European Union (EU) (1999). Council Directive 1999/30/EC of 22 April 1999 relating to limit values for sulphur dioxide, nitrogen dioxide and oxides of nitrogen, particulate matter and lead in ambient air. Official Journal of the European Communities.

EN (2005). Ambient air quality–standard gravimetric measurement method for the determination of the PM2.5 mass fraction of suspended particulate matter. Comité Européen de Normalisation (CEN; European Committee for Standardization).

Feng, S., Gao, D., Liao, F., Zhou, F., Wang, X. (2016). The health effects of ambient PM2.5 and potential mechanisms. Ecotoxicol. Environ. Saf. 128, 67–74. https://doi.org/10.1016/j.ecoenv.2016.01.030

Grimm, H., Eatough, D.J. (2009). Aerosol measurement: The use of optical light scattering for the determination of particulate size distribution, and particulate mass, including the semi-volatile fraction. J. Air Waste Manage. Assoc. 59, 101–107. https://doi.org/10.3155/1047-3289.59.1.101

Hand, J.L., Malm, W.C. (2007). Review of aerosol mass scattering efficiencies from ground-based measurements since 1990. J. Geophys. Res. 112, D16203. https://doi.org/10.1029/2007JD008484

Holstius, D.M., Pillarisetti, A., Smith, K.R., Seto, E. (2014). Field calibrations of a low-cost aerosol sensor at a regulatory monitoring site in california. Atmos. Meas. Tech. 7, 605–632. https://doi.org/10.5194/amtd-7-605-2014

Huang, C.H. (2007). Field comparison of real-time PM2.5 readings from a beta gauge monitor and a light scattering method. Aerosol Air Qual. Res. 7, 239–250. https://doi.org/10.4209/aaqr.2007.01.0002

Kim, K.H., Kabir, E., Kabir, S. (2015). A review on the human health impact of airborne particulate matter. Environ. Int. 74, 136–143. https://doi.org/10.1016/j.envint.2014.10.005

Kim, K.W. (2015). Optical properties of size-resolved aerosol chemistry and visibility variation observed in the urban site of Seoul, Korea. Aerosol Air Qual. Res 15, 271–283. https://doi.org/10.4209/aaqr.2013.11.0347

Kulkarni, P., Baron, P.A., Willeke, K. (2011). Aerosol measurement: Principles, techniques, and applications. John Wiley & Sons. https://doi.org/10.1002/9781118001684

Liu, L.J.S., Slaughter, J.C., Larson, T.V. (2002). Comparison of light scattering devices and impactors for particulate measurements in indoor, outdoor, and personal environments. Environ. Sci. Technol. 36, 2977–2986. https://doi.org/10.1021/es0112644

Melki, P.N., Ledoux, F., Aouad, S., Billet, S., El Khoury, B., Landkocz, Y., Abdel-Massih, R.M., Courcot, D. (2017). Physicochemical characteristics, mutagenicity and genotoxicity of airborne particles under industrial and rural influences in northern Lebanon. Environ. Sci. Pollut. Res. 24, 18782–18797. https://doi.org/10.1007/s11356-017-9389-3

Müller, T., Nowak, A., Wiedensohler, A., Sheridan, P., Laborde, M., Covert, D.S., Marinoni, A., Imre, K., Henzing, B., Roger, J.C., dos Santos, S.M., Wilhelm, R., Wang, Y.Q., de Leeuw, G. (2009). Angular illumination and truncation of three different integrating nephelometers: Implications for empirical, size-based corrections. Aerosol Sci. Technol. 43, 581–586. https://doi.org/10.1080/02786820902798484

Nagel, G., Stafoggia, M., Pedersen, M., Andersen, Z.J., Galassi, C., Munkenast, J., Jaensch, A., Sommar, J., Forsberg, B., Olsson, D., Oftedal, B., Krog, N.H., Aamodt, G., Pyko, A., Pershagen, G., Korek, M., Faire, U.D., Pedersen, N.L., Östenson, C.G., … Weinmayr, G. (2018). Air pollution and incidence of cancers of the stomach and the upper aerodigestive tract in the European Study of Cohorts for Air Pollution Effects (ESCAPE). Int. J. Cancer. 143, 1632–1643. https://doi.org/10.1002/ijc.31564

Sioutas, C., Kim, S., Chang, M., Terrell, L.L., Gong, H. Jr. (2000). Field evaluation of a modified dataram mie scattering monitor for real-time PM2.5 mass concentration measurements. Atmos. Environ. 34, 4829–4838. https://doi.org/10.1016/S1352-2310(00)00244-2

Thunis, P., Degraeuwe, B., Pisoni, E., Trombetti, M., Peduzzi, E., Belis, C., Wilson, J., Vignati, E. (2017). Urban PM2.5 atlas–air quality in european cities. Retrieved from Luxemboug.

Tryner, J., Good, N., Wilson, A., Clark, M.L., Peel, J.L., Volckens, J. (2019). Variation in gravimetric correction factors for nephelometer-derived estimates of personal exposure to PM2.5. Environ. Pollut. 250, 251–261. https://doi.org/10.2760/336669

Vardoulakis, S., Davis, A., Steinle, S., Sleeuwenhoek, A., Galea, K., Dixon, K., Crawford, J. (2019). Indoor exposure to selected air pollutants and associated health effects: A global review. Environ. Epidemiol. 3, 410. https://doi.org/10.1097/01.EE9.0000610588.36432.08

Vincent, J.H. (2007). Aerosol sampling: Science, standards, instrumentation and applications. John Wiley & Sons.

von der Weiden, S.L., Drewnick, F., Borrmann, S. (2009). Particle loss calculator – A new software tool for the assessment of the performance of aerosol inlet systems. Atmos. Meas. Tech. 2, 479–494. https://doi.org/10.5194/amt-2-479-2009

Waggoner, A.P., Weiss, R.E. (1980). Comparison of fine particle mass concentration and light scattering extinction in ambient aerosol. Atmos. Environ. 14, 623–626. https://doi.org/10.1016/0004-6981(80)90098-0

Zieger, P., Fierz-Schmidhauser, R., Weingartner, E., Baltensperger, U. (2013). Effects of relative humidity on aerosol light scattering: Results from different European sites. Atmos. Chem. Phys. 13: 10631–10609. https://doi.org/10.5194/acp-13-10609-2013

Share this article with your colleagues 


Subscribe to our Newsletter 

Aerosol and Air Quality Research has published over 2,000 peer-reviewed articles. Enter your email address to receive latest updates and research articles to your inbox every second week.

81st percentile
Powered by

2020 Impact Factor: 3.063
5-Year Impact Factor: 2.857

Aerosol and Air Quality Research (AAQR) is an independently-run non-profit journal that promotes submissions of high-quality research and strives to be one of the leading aerosol and air quality open-access journals in the world. We use cookies on this website to personalize content to improve your user experience and analyze our traffic. By using this site you agree to its use of cookies.