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Frontiers of Earth Science

ISSN 2095-0195

ISSN 2095-0209(Online)

CN 11-5982/P

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Front. Earth Sci.    2021, Vol. 15 Issue (1) : 133-150    https://doi.org/10.1007/s11707-020-0814-4
RESEARCH ARTICLE
Spatial study of particulate matter distribution, based on climatic indicators during major dust storms in the State of Arizona
Amin MOHEBBI1(), Fan YU1, Shiqing CAI1, Simin AKBARIYEH2, Edward J. SMAGLIK1
1. Department of Civil Engineering, Construction Management, and Environmental Engineering, Northern Arizona University, Flagstaff, AZ 86011, USA
2. Department of Civil and Environmental Engineering, California Polytechnic State University, San Luis Obispo, CA 93407, USA
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Abstract

Arizona residents have been dealing with the suspended particulate matter caused health issues for a long time due to Arizona’s arid climate. The state of Arizona is vulnerable to dust storms, especially in the monsoon season because of the anomalies in wind direction and magnitude. In this study, a high-resolution Weather Research and Forecasting (WRF) model coupled with a chemistry module (WRF-Chem) was simulated to compute the particulate matter spatiotemporal distribution as well as the climatic parameters for the state of Arizona. Subsequently, Ordinary Least Square (OLS), spatial lag, spatial error, and Geographically Weighted Regression (GWR) techniques were utilized to develop predictive models based on the climatic indicators that impacted the formation and dispersion of the particulate matter during dust storms. Census tracts were adopted to create local spatial averages for the chosen variables. Terrain height, temperature, wind speed, and vegetation fraction were designated as the most significant variables, whereas base state and perturbation pressures, planetary boundary layer height and soil moisture were adopted as supplementary variables. The determination coefficient for OLS, spatial lag, spatial error, and GWR models peaked at 0.92, 0.93, 0.96, and 0.97, respectively. These models provide a better understanding of the current distribution of the particulate matter and can be used to forecast future trends.
Keywords particulate matter      dust storm      Weather Research and Forecasting      census tracts      Ordinary Least Square      Geographically Weighted Regression     
Corresponding Author(s): Amin MOHEBBI   
Online First Date: 23 April 2020    Issue Date: 19 April 2021
 Cite this article:   
Amin MOHEBBI,Fan YU,Shiqing CAI, et al. Spatial study of particulate matter distribution, based on climatic indicators during major dust storms in the State of Arizona[J]. Front. Earth Sci., 2021, 15(1): 133-150.
 URL:  
https://academic.hep.com.cn/fesci/EN/10.1007/s11707-020-0814-4
https://academic.hep.com.cn/fesci/EN/Y2021/V15/I1/133
Fig.1  Project process.
Fig.2  Study area.
Official WRF name Description Dimension Unit
P10 particulate matter with a diameter less than 10 μm 4D μg·m−3
P2_5_DRY particulate matter with a diameter less than 2.5 μm 4D μg·m−3
HGT terrain height from mean sea level 3D m
T2 temperature at 2 m above ground 3D K
U10 wind speed at 10 m above ground in west-east direction 3D m·s−1
V10 wind speed at 10 m above ground in north-south direction 3D m·s−1
VEGFRA vegetation fraction 3D
P perturbation pressure 4D Pa
PB base state pressure 4D Pa
PBLH planetary boundary layer height 3D m
SMOIS soil moisture at four layers 4D
Tab.1  Hydrological and climatic variables influencing particulate matter formation and transport
Fig.3  Significant explanatory variables during July 4th, 2014 dust storm. The variables are averaged temporally for the duration of the simulation and spatially for each census tract.
Fig.4  Supplementary explanatory variable during July 4th, 2014 dust storm. The variables are averaged temporally for the duration of the simulation and spatially for each census tract.
Case Model start date Model end date PM10 (μg·m−3) PM2.5 (μg·m−3)
Simulated Observed Simulated Observed
A Jul 10, 2009 Jul 15, 2009 ?2344.5 ?2133.5 ?395.9 ?360.3
B Jan 19, 2010 Jan 24, 2010 17506.3 17156.2 2912.3 2825.0
C Jul 3, 2011 Jul 8, 2011 ?4032.4 ?3992.0 ?682.1 ?668.5
D Jul 19, 2012 Jul 24, 2012 ??687.4 ??680.5 ?115.7 ?120.3
E Jun 28, 2013 Jul 3, 2013 ?6265.7 ?5451.2 1057.0 ?972.4
F Jul 1, 2014 Jul 6, 2014 ?5992.6 ?6172.3 1021.0 1051.7
G Jul 6, 2014 Jul 11, 2014 ?1927.4 ?1946.6 ?328.2 ?334.7
H Jun 26, 2015 Jul 1, 2015 ?1987.1 ?2006.9 ?334.4 ?294.3
I Aug 13, 2016 Aug 18, 2016 ??498.3 N/A ??83.5 N/A
Tab.2  Maximum PM10 and PM2.5 for the duration of simulation averaged over census tracts
Fig.5  Comparison of PM10 simulated by WRF-Chem and PM10 observed in Pinal County, AZ air quality stations.
Fig.6  Normalized mean PM10 modeled by WRF-Chem averaged temporally for the duration of the simulation and spatially for each census tract.
Fig.7  Normalized PM10 calculated by classical OLS versus modeled PM10, the dashed red line represents a one to one match, and the solid blue line represents the best linear fit.
Case r2 AIC Log (L)
Classic Spatial lag Spatial error Classic Spatial lag Spatial error Classic Spatial lag Spatial error
A 0.92 0.93 0.96 8408 7680 8408 -4194 -4144 -3830
B 0.76 0.83 0.89 15971 15475 15061 -7976 -7727 -7521
C 0.92 0.92 0.95 12772 12689 12023 -6376 -6333 -6002
D 0.75 0.76 0.79 3689 3659 3527 -1835 -1818 -1754
E 0.89 0.91 0.95 14250 13943 13286 -7115 -6960 -6633
F 0.87 0.88 0.92 14469 14312 13824 -7224 -7145 -6902
G 0.89 0.90 0.93 9091 8989 8583 -4536 -4483 -4281
H 0.91 0.91 0.94 9701 9663 9125 -4841 -4820 -4552
I 0.85 0.90 0.93 2734 2234 1823 -1357 -1106 -901
Tab.3  Classic, spatial lag, and spatial error performance comparison
Case C_HGT C_T2 C_U10 C_V10 C_VEGFRA C_P C_PB C_PBLH C_SMOIS
A 0.02 2.60 0.61 -0.49 0.16 -0.13 -0.01 -0.03 -136.18
B 0.52 -28.69 35.92 0.83 -0.24 -0.14 0.09 -0.14 -343.65
C 0.43 -19.75 -16.77 18.18 -0.25 0.10 0.06 -0.05 -973.55
D 0.03 -1.23 0.05 1.07 -0.04 0.00 0.00 0.01 -22.45
E 0.75 -15.55 29.01 9.58 -0.80 -0.50 0.06 -0.38 0.55
F 1.49 -40.72 -11.31 30.19 -1.95 -0.78 0.14 -0.15 533.50
G 0.12 -1.22 7.78 2.58 0.20 -0.19 0.01 -0.01 -171.22
H 0.10 -2.69 7.88 1.64 0.30 -0.08 0.01 -0.01 -1.86
I 0.00 -0.13 0.00 0.28 0.01 0.00 0.00 0.01 -10.60
Tab.4  Coefficients used in the classic OLS regression for PM10
Fig.8  Comparison of PM10 calculated by Eq. 18 and Eq. 19, for all the case studies.
Fig.9  GWR determination coefficient and residuals.
Fig.10  GWR coefficients for PM10.
Fig.11  Cluster map of OLS and GER residuals.
Cluster Count Area/ %
OLS GWR OLS GWR
H-H 69 40 24% 6%
L-L 108 55 23% 10%
H-L 5 4 1% 1%
L-H 5 7 0% 2%
Not significant 1339 1420 51% 81%
Sum 1526 1526 100% 100%
Tab.5  OLS and GWR local spatial autocorrelation
1 H Akaike (1998). Information theory and an extension of the maximum likelihood principle. In: Selected papers of Hirotugu Akaike. New York: Springer: 199–213
2 P D Allison (1999). Multiple Regression: A Primer. New York: Pine Forge Press
3 L Anselin (2002). Under the hood issues in the specification and interpretation of spatial regression models. Agricultural economics, 27(3): 247–267
4 L Anselin (2013). Spatial Econometrics: Methods and Models. New York: Springer Science & Business Media
5 S P Arya (1999). Air Pollution Meteorology and Dispersion. New York: Oxford University Press
6 I Barmpadimos, C Hueglin, J Keller, S Henne, A S H Prévôt (2011). Influence of meteorology on PM10 trends and variability in Switzerland from 1991 to 2008. Atmospheric Chemistry and Physics, 11(4): 1813–1835
7 L Bruyere, J Monaghan , F Steinhoff, D Yates (2015). Bias-corrected CMIP5 CESM data in WRF/MPAS intermediate file format. Climate Dynamics, 2014, 43(7–8): 1847–1856
8 Census (2010). Geography Program. United States Census Bureau
9 R Davidson, J G MacKinnon (2004). Econometric Theory and Methods. New York: Oxford University Press
10 S Fuzzi, U Baltensperger, K Carslaw, S Decesari, H Denier Van Der Gon, M C Facchini, D Fowler, I Koren, B Langford, U Lohmann, E Nemitz, S Pandis, I Riipinen, Y Rudich, M Schaap, J Slowik, D V Spracklen, E Vignati, M Wild, M Williams, S Gilardoni (2015). Particulate matter, air quality and climate: lessons learned and future needs. Atmos Chem Phys, 15(14): 8217–8299
11 A Getis, J K Ord (1992). The analysis of spatial association by use of distance statistics. Geogr Anal, 24(3): 189–206
12 P Ginoux, M Chin, I Tegen, J M Prospero, B Holben, O Dubovik, S J Lin (2001). Sources and distributions of dust aerosols simulated with the GOCART model. J Geoph Res: Atmos, 106(D17): 20255–20273
13 G A Grell, S E Peckham, R Schmitz, S A McKeen, G Frost, W C Skamarock, B Eder (2005). Fully coupled ‘online’ chemistry within the WRF model. Atmos Environ, 39(37): 6957–6975
https://doi.org/10.1016/j.atmosenv.2005.04.027
14 J F Hair (2006). Multivariate Data Analysis. India: Pearson Education
15 P Hyde, A Mahalov, J Li (2018). Simulating the meteorology and PM10 concentrations in Arizona dust storms using the Weather Research and Forecasting model with Chemistry (Wrf-Chem). J Air Waste Manag Assoc, 68(3): 177–195
https://doi.org/10.1080/10962247.2017.1357662 pmid: 28738173
16 A Karambelas (2013). The interactions of biogenic and anthropogenic gaseous emissions with respect to aerosol formation in the United States. Dissertation for Doctoral Degree. Madison: University of Wisconsin
17 S Lee, C H Ho, Y S Choi (2011). High-PM10 concentration episodes in Seoul, Korea: background sources and related meteorological conditions. Atmos Environ, 45(39): 7240–7247
https://doi.org/10.1016/j.atmosenv.2011.08.071
18 S L LeGrand, C Polashenski, T W Letcher, G A Creighton, S E Peckham, J D Cetola (2019). The AFWA dust emission scheme for the GOCART aerosol model in WRF-Chem v3. 8.1. Geosci Model Dev, 12(1): 131–166
19 B Mamtimin, F X Meixner (2011). Air pollution and meteorological processes in the growing dryland city of Urumqi (Xinjiang, China). Sci Total Environ, 409(7): 1277–1290
https://doi.org/10.1016/j.scitotenv.2010.12.010 pmid: 21239046
20 J Michalakes, S Chen, J Dudhia, L Hart, J Klemp, J Middlecoff, W Skamarock (2001). Development of a next-generation regional weather research and forecast model. Developments in Teracomputing, 269–276
21 A Mohebbi, H I Chang, D Hondula (2017). WRF-Chem model simulations of Arizona Dust Storms. AGU Fall Meeting Abstracts
22 A Mohebbi, G T Green, S Akbariyeh, F Yu, B J Russo, E J Smaglik (2019). Development of dust storm modeling for use in freeway safety and operations management: an Arizona case study. Transportation Research Record, 2673(5): 175–187
23 P A P Moran (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1–2): 17–23
https://doi.org/10.1093/biomet/37.1-2.17 pmid: 15420245
24 M Natrella (2013). NIST/SEMATECH E-handbook of Statistical Methods. National Institute of Standards and Technology
25 PCAQCD (2016). Pinal County Air Quality Control District. Pinal County, Florence, AZ
26 J Rost, T Holst, E Sähn, M Klingner, K Anke, D Ahrens, H Mayer (2009). Variability of PM10 concentrations dependent on meteorological conditions. Int J Environ Pollut., 36(1–3): 3–18
27 A Sfetsos, D Vlachogiannis (2010). A new approach to discovering the causal relationship between meteorological patterns and PM10 exceedances. Atmos Res, 98(2–4): 500–511
https://doi.org/10.1016/j.atmosres.2010.08.021
28 Y Shao, M Ishizuka, M Mikami, J F Leys (2011). Parameterization of size-resolved dust emission and validation with measurements. Int J Environ Pollut, 116(D8): D08203
https://doi.org/10.1029/2010JD014527
29 W C Skamarock, J B Klemp, J Dudhia, D O Gill, D M Barker, W Wang, J G Powers (2008). A Description of the Advanced Research WRF version 3. Tech Note, 1–96
30 M J De Smith, M F Goodchild, P Longley (2007). Geospatial Analysis: A Comprehensive Guide to Principles, Techniques and Software Tools. Leicester: Troubador Publishing Ltd
31 G Tian, Z Qiao, X Xu (2014). Characteristics of particulate matter (PM10) and its relationship with meteorological factors during 2001–2012 in Beijing. Environ Pollut, 192: 266–274
32 US climate data (2019). Climate Arizona-Phoetb. U.S. Climate Data
33 W Wang, C Bruyere, M Duda, J Dudhia, D Gill, H C Lin, J Michaelakes, S Rizvi, X Zhang (2009). WRF-ARW Version 3 Modeling System User’s Guide. Mesoscale & Microscale Meteorology Division. Boulder: National Center for Atmospheric Research
34 E K Wise, A C Comrie (2005). Meteorologically adjusted urban air quality trends in the southwestern United States. Atmos Environ, 39(16): 2969–2980
https://doi.org/10.1016/j.atmosenv.2005.01.024
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