Proton Beam-Induced X Ray Emission (PIXE)
PIXE measurements of PI and IB environmental samples show that Fe, Si, Al and Ca are the predominant elements present in their composition (Fig. 2). Other elements such as P, S, Cl, K, Ti, Na, Cu, Mn and Mg are found in a concentration less than 1%. It is also noticeable that there is a variation along the year both in IB and PI, which can be related to seasonal climate variations, such as: wind velocity and direction, pluviosity and temperature.
IB samples contain larger percentage of Fe and Si, and less Ca than PI samples. During the first quarter of the year IB samples exhibit the largest content of Fe, while PI samples exhibit the lowest. This fact may be related to the wind being predominantly in the northeast direction during this period of the year, together with low pluviosity and increased solar radiation.
Thermal Optical Transmittance (TOT)
Analyses of carbon content based on TOT protocol IMPROVE A are widely used for environmental studies due to their repeatability and comparability of the results, being mainly used for the quantification of organic and elemental carbon fractions (DRI SOP, 2005). OC and EC fractions were obtained following the operating procedure indicated in references (DRI SOP, 2005; Chow et al., 2007). The organic carbon (OC) mass percentage is obtained from the carbon evolved from the filter punch in a He-only (>99.999%) atmosphere. OC1 was obtained in the range between RT and 140°C, OC2 was obtained at 280°C, OC3 at 480°C and OC4 between 480 and 580°C plus pyrolyzed organic carbon. This is the same as volatile organic carbon (VOC) plus high-temperature OC. The elemental carbon (EC) mass percentage is obtained from the carbon evolved from the filter punch in a 98%(He)/2%(O2) atmosphere. EC1 is obtained up to 580°C. EC2 and EC3 are obtained, respectively, at 740°C and 840°C after charring correction (Chow et al., 2007). The carbon fraction uncertainty is 5%.
The greatest contribution from coal is evidenced by OC3, EC1 and EC2 fractions. The coke source is more represented by EC2 and EC3. CP-AMT source is represented by the EC2 and EC3 fractions (from coke production), but the possible combination with OC3, EC1 and EC2, from the coal input, must be considered (Fig. 3 and 4). Fig.3 presents the most important results of OC and EC obtained by TOT. Only those fractions greater than 0.1 wt% are shown. The mass percentage OC and EC carbon content for all main source samples considered in this study is shown as Table 2. Note that source VENT2 results from secondary dedusting systems used in the basic oxygen furnaces at the steel factory of ArcelorMittal Tubarão. This source was not included as a component of the AMT Chemical profile shown in Table 1.
Table 2.
Distribution of carbon fractions (wt%) in the sources obtained by TOT.
Sources
|
OC1
|
OC2
|
OC3
|
OC4
|
EC1
|
EC2
|
EC3
|
Coal
|
0.41
|
0.68
|
10.73
|
0.00
|
18.16
|
40.71
|
1.71
|
Coke
|
0.01
|
0.02
|
0.35
|
0.00
|
3.33
|
43.69
|
31.99
|
CP-AMT
|
0.01
|
0.02
|
1.01
|
0.00
|
1.86
|
5.89
|
2.61
|
ESKR
|
0.01
|
0.02
|
0.70
|
0.09
|
0.63
|
0.72
|
0.32
|
VENT2
|
0.00
|
0.01
|
0.38
|
0.05
|
0.19
|
0.37
|
0.56
|
Pellets
|
0.01
|
0.03
|
0.20
|
0.00
|
0.04
|
0.32
|
0.33
|
ESLD
|
0.00
|
0.00
|
0.44
|
0.04
|
0.02
|
0.08
|
0.03
|
Soil
|
0.06
|
0.01
|
0.23
|
0.00
|
0.03
|
0.02
|
0.02
|
Iron Ore
|
0.00
|
0.00
|
0.07
|
0.00
|
0.02
|
0.03
|
0.02
|
The environmental samples studied show a greater participation of fractions EC1 and EC2, being larger for those collected in point (PI) than for those collected in point (IB). Along 2018 and 2019 the observed trends for each quarter are similar (Fig. 4). When they are compared to carbon apportionment of the sources, there is a participation of coal, coke and CP-AMT sources. Note that both coal and coke are used in the industrial production of the steel industry.
Table 3 summarizes the main results obtained by PIXE and TOT corresponding to the wt% element composition of all sources considered. The wt% of organic carbon and elemental carbon (Total OC and Total EC) correspond to the total fractions obtained by TOT for each sample, independent of subfractions evolved during the analyses. Such information is used to build a chemical profile for each source, together with XRD and MS characterization, which are used to enhance the chemical profiles for sources and environmental samples.
Table 3.
Summary of the element composition of the sources (wt%), obtained by PIXE and TOT.
Sources
|
Elements
|
Na
|
Mg
|
Al
|
Si
|
S
|
Cl
|
K
|
Ca
|
Mn
|
Fe total
|
Total OC
|
Total EC
|
Soil
|
0.01
|
0.03
|
11.08
|
25.75
|
0.04
|
0.09
|
0.04
|
2.49
|
0.07
|
5.98
|
0.30
|
0.08
|
Sea
|
40.00
|
0.00
|
0.00
|
0.00
|
3.30
|
40.00
|
1.40
|
1.40
|
0.00
|
0.00
|
0.00
|
0.00
|
Iron Ore
|
0.10
|
0.09
|
0.11
|
5.27
|
0.07
|
0.00
|
0.01
|
0.91
|
0.04
|
51.97
|
0.07
|
0.07
|
Coal
|
0.03
|
0.05
|
0.97
|
1.63
|
0.32
|
0.03
|
0.10
|
0.30
|
0.00
|
0.46
|
11.82
|
60.57
|
Pellets
|
0.18
|
0.16
|
0.63
|
2.98
|
0.09
|
0.02
|
0.02
|
1.62
|
0.05
|
53.22
|
0.24
|
0.69
|
Coke
|
0.03
|
0.14
|
0.99
|
1.84
|
0.16
|
0.02
|
0.07
|
0.71
|
0.05
|
1.09
|
0.38
|
79.02
|
ESLD
|
0.00
|
4.60
|
3.10
|
7.28
|
0.28
|
0.04
|
0.03
|
26.00
|
1.59
|
12.64
|
0.47
|
0.13
|
ESKR
|
0.00
|
1.33
|
3.09
|
6.25
|
2.52
|
0.12
|
0.00
|
32.04
|
0.45
|
6.75
|
0.82
|
1.67
|
CP-AMT
|
0.04
|
2.49
|
1.18
|
2.78
|
0.26
|
0.00
|
0.06
|
12.96
|
0.60
|
13.58
|
1.05
|
10.35
|
VENT2
|
0.00
|
4.15
|
0.69
|
1.04
|
0.71
|
1.10
|
1.00
|
19.64
|
0.84
|
26.73
|
0.45
|
1.49
|
X-Ray Diffraction (XRD)
The XRD patterns for (IB) and (PI) environmental samples collected in 2019 are shown in Fig. 5. IB samples (Fig. 5a) show the predominance of hematite (Fe2O3), quartz (SiO2) and microcline (KAlSi3O8). Minerals such as calcite (CaCO3), rutile (TiO2) and muscovite (KAl2 (AlSi3O10) (OH)2) were also identified. There are small differences between IB and PI samples, however, PI samples (Fig. 5b), exhibit the predominance of quartz, probably originated from natural soil, kaolinite (Al2Si2O5(OH)4), calcite and hematite (Fe2O3).
Similar crystalline phases and proportions were observed for 2018 and 2019 samples. Given that XRD is not able to distinguish iron oxides with low degree of crystallinity, 57Fe Mössbauer spectroscopy measurements were done to differentiate such phases.
57Fe Mössbauer Spectroscopy
57Fe Mössbauer spectroscopy is a highly selective technique, which can detect very subtle changes in the chemical environment of the 57Fe atom, allowing to obtain information regarding the various Fe compounds contained in the sample, non-equivalent Fe crystallographic sites, oxidation states, chemical bonds, and magnetic properties, among others. Besides, the high energy ~106 eV and the narrow line width of gamma radiation energy allows an extremely high energy resolution, of few parts in 1011 (Gonser, 1975). These features make Mössbauer spectroscopy an essential non-destructive technique, since it allows the distinction between the iron oxides present in soil, iron ore and PM samples, enabling Fe to be used as a tracer element to assess the contribution of each source (Tavares et al., 2017).
The Mössbauer spectra were obtained in transmission mode at room temperature and at 25 K, using a conventional spectrometer, with constant acceleration and 57Co source embedded in a Rh matrix. Isomer shift values quoted are relative to metallic α-Fe. The experimental data, represented by dots and black line, and fitted subspectra are shown in Figs. 6 and 7. The hyperfine parameters used to fit the data are shown in Tables 4 and 5. Mössbauer spectra measured at room temperature (RT) were fitted with two sets of sub-spectra: i) two doublets to account for the central lines in the spectrum and ii) one sextet that account for the remaining six lines. The first set of subspectra was attributed to iron silicates (Si-Fe) and iron oxides corresponding to very small particles, having mean diameter in the nanometer range, that exhibit superparamagnetic behavior at RT, and the sextet subspectrum was attributed to magnetic iron oxides which may exhibit antiferromagnetic (AF) or weekly ferromagnetic (WF) behaviour (Murad et al., 1986; Greenwood et al., 1971).
In order to avoid superparamagnetic behavior of the particles at room temperature and improve the resolution of the hyperfine magnetic parameters, low temperature (25 K) measurements were required. As the temperature is reduced the superparamagnetic particles are blocked, they are less susceptible to thermal disorder, and tend to order magnetically. This allows to obtain information about the different phases existing in the material studied. The spectra measured at 25 K were fitted with a doublet subspectra, associated to iron silicate minerals ([(Ca,Fe2+)3(Al,Fe3+)2(SiO4)3]) and a set of four sub-spectra associated to soil hematite (α-Fe2O3*), iron ore (α-Fe2O3) (Tavares et al., 2017), goethite (α-FeOOH), and ferrihydrite (Fe5HO8.4H2O) (Tavares et al., 2017; Murad et al., 1986; Greenwood et al., 1971, Murad et al., 1980).
The identification of soil hematite and iron ore hematite is based in the Morin transition (TM) that occurs at TM » 250 K for hematite (Morin, 1950; Özdemir et al, 2008). Below TM, spins lie along the trigonal or c-axis of the rhombohedral lattice, forming an AF spin structure. This AF ordering changes to weakly ferromagnetic (WF) on heating above TM, as the spins flip 90º to the basal c plane and arrange out of exact antiparallelism, by a fraction of a degree (Özdemir et al, 2008). Such canted arrangement results in WF behavior. When hematite experiences such transition upon cooling below TM, there is a change of the algebraic sign of the quadrupolar displacement, from negative value at RT, i.e. above TM, to a positive value below TM (e.g. at 25 K). Therefore, at room temperature, quadrupolar displacement values (2ξq) are typically negative for well-crystallized hematite, as found in iron ore. When the hematite does not undergo the Morin transition the quadrupolar shift remains negative below TM. This is typically observed in soil hematite (Tavares et al., 2017). Hence, in the case of iron-rich samples, the quadrupolar displacement below TM can be taken as a primary signature pointing to the origin of the hematite contained in the sample studied, and therefore to a possible source of PM contained in that sample.
Table 4.
Hyperfine parameters corresponding to Mössbauer spectra measured at RT and 25K for IB samples collected in 2019.
Samples
|
Temp. (K)
|
Phase
|
d (± 0,05 mm/s)
|
D/2ξq (± 0,05 mm/s)
|
Hhf (± 0,2 T)
|
Area (± 1%)
|
IB1Q19
|
RT
|
Mix(a-Fe2O3, a-FOOH)
|
0.36
|
-0.21
|
51.7
|
94
|
SPM
|
0.33
|
0.63
|
|
6
|
25K
|
a-Fe2O3
|
0.47
|
0.41
|
54.2
|
72
|
a-Fe2O3*
|
0.48
|
-0.06
|
53.0
|
15
|
a-FeOOH
|
0.41
|
-0.07
|
49.8
|
8
|
Fe5HO8.4H2O
|
0.43
|
0.09
|
44.7
|
3
|
(Ca,Fe2+)3(Al,Fe3+)2(SiO4)3
|
0.39
|
0.69
|
|
2
|
IB2Q19
|
RT
|
Mix(a-Fe2O3, a-FOOH)
|
0.36
|
-0.19
|
51.9
|
86
|
SPM
|
0.34
|
0.74
|
|
14
|
25K
|
a-Fe2O3
|
0.47
|
0.42
|
54.1
|
70
|
a-Fe2O3*
|
0.44
|
-0.08
|
53.1
|
17
|
a-FeOOH
|
0.44
|
-0.05
|
49.6
|
6
|
Fe5HO8.4H2O
|
0.44
|
-0.02
|
44.1
|
3
|
(Ca,Fe2+)3(Al,Fe3+)2(SiO4)3
|
0.39
|
0.65
|
|
4
|
IB3Q19
|
RT
|
Mix(a-Fe2O3, a-FOOH)
|
0.36
|
-0.20
|
51.7
|
86
|
SPM
|
0.33
|
0.77
|
|
14
|
25 K
|
a-Fe2O3
|
0.48
|
0.40
|
54.1
|
69
|
a-Fe2O3*
|
0.42
|
-0.09
|
52.9
|
16
|
a-FeOOH
|
0.41
|
-0.06
|
49.8
|
6
|
Fe5HO8.4H2O
|
0.43
|
-0.01
|
45.4
|
4
|
(Ca,Fe2+)3(Al,Fe3+)2(SiO4)3
|
0.38
|
0.71
|
|
5
|
IB4Q19
|
RT
|
Mix(a-Fe2O3, a-FOOH)
|
0.36
|
-0.26
|
51.8
|
91
|
SPM
|
0.34
|
0.69
|
|
9
|
25 K
|
a-Fe2O3
|
0.49
|
0.41
|
54.6
|
71
|
a-Fe2O3*
|
0.41
|
-0.04
|
53.5
|
12
|
a-FeOOH
|
0.43
|
-0.10
|
50.9
|
12
|
Fe5HO8.4H2O
|
0.40
|
0.08
|
46.1
|
2
|
(Ca,Fe2+)3(Al,Fe3+)2(SiO4)3
|
0.4
|
0.76
|
|
3
|
Note: α-Fe2O3* = LC-hematite. α-Fe2O3 = HC-hematite; d = isomer shift relative to aFe; ∆ = quadrupole splitting; 2ξq = quadrupole shift; Hhf = hyperfine magnetic field; area = relative spectral areas
Table 5.
Parameters corresponding to Mössbauer spectra measured at RT and 25K for PI samples collected in 2019.
Samples
|
T (K)
|
Phase
|
d (± 0.05 mm/s)
|
D/2ξq (± 0.05 mm/s)
|
Hhf (± 0.2 T)
|
Area (± 1%)
|
PI1Q19
|
RT
|
Mix(a-Fe2O3
a-FOOH
|
0.36
0.33
|
-0.21
0.09
|
51.9
46.8
|
72
17
|
SPM
|
0.34
|
0.71
|
|
11
|
25 K
|
a-Fe2O3
|
0.47
|
0.39
|
54.3
|
51
|
a-Fe2O3*
|
0.49
|
-0.06
|
52.9
|
17
|
a-FeOOH
|
0.41
|
-0.07
|
50.4
|
17
|
Fe5HO8.4H2O
|
0.44
|
-0.09
|
44.4
|
7
|
(Ca,Fe2+)3(Al,Fe3+)2(SiO4)3
|
0.33
|
0.76
|
|
8
|
PI2Q19
|
RT
|
Mix(a-Fe2O3
a-FOOH
|
0.33
0.34
|
-0.19
0.07
|
51.9
44.8
|
65
10
|
SPM
|
0.33
|
0.78
|
|
25
|
25 K
|
a-Fe2O3
|
0.47
|
0.39
|
54.2
|
52
|
a-Fe2O3*
|
0.49
|
-0.06
|
52.3
|
16
|
a-FeOOH
|
0.49
|
-0.07
|
50.3
|
11
|
Fe5HO8.4H2O
|
0.47
|
-0.09
|
45.5
|
6
|
(Ca,Fe2+)3(Al,Fe3+)2(SiO4)3
|
0.40
|
0.76
|
|
15
|
PI3Q19
|
RT
|
Mix(a-Fe2O3
a-FOOH
|
0.33
0.34
|
-0.21
0.06
|
51.7
45.8
|
70
19
|
SPM
|
0.33
|
0.72
|
|
11
|
25 K
|
a-Fe2O3
|
0.48
|
0.39
|
54.1
|
61
|
a-Fe2O3*
|
0.44
|
-0.02
|
52.8
|
16
|
a-FeOOH
|
0.45
|
-0.06
|
49.9
|
7
|
Fe5HO8.4H2O
|
0.44
|
-0.01
|
46.3
|
7
|
(Ca,Fe2+)3(Al,Fe3+)2(SiO4)3
|
0.40
|
0.81
|
|
9
|
PI4Q19
|
RT
|
Mix(a-Fe2O3
a-FOOH
|
0.36
0.34
|
-0.21
-0.10
|
51.7
45.5
|
75
7
|
SPM
|
0.33
|
0.64
|
|
18
|
25 K
|
a-Fe2O3
|
0.48
|
0.40
|
45.6
|
56
|
a-Fe2O3*
|
0.49
|
-0.06
|
52.9
|
20
|
a-FeOOH
|
0.41
|
-0.14
|
49.9
|
13
|
Fe5HO8.4H2O
|
0.40
|
0.05
|
45.9
|
6
|
(Ca,Fe2+)3(Al,Fe3+)2(SiO4)3
|
0.40
|
0.83
|
-
|
5
|
Note: α-Fe2O3* = LC-hematite. α-Fe2O3 = HC-hematite.; d = isomer shift relative to aFe; ∆ = quadrupole splitting; 2ξq = quadrupole shift; Bhf = hyperfine magnetic field; area = relative spectral areas
The relative area under the curve for each subspectra to the total spectrum area is proportional to the apportionment of the corresponding material. Following this, it can be inferred from Fig. 8 the presence of both types of hematite in the environmental samples. This is a very important result, since it reveals that the sources of emission contributing to the PM collected in those samples are both from natural origin (e.g. soils, rocks, sands) as well as of anthropic origins (e.g. industries). Another interesting observation is that the hematite content has significant variations depending on the quarter/year and the region where the samples were collected.
PI samples collected in 2018 show a significant increase in the spectral area of Fe3+, iron silicate mineral (Ca,Fe2+)3(Al,Fe3+)2(SiO4)3 phases. Regarding 2019 samples, no major variations in the relative spectral area were observed compared to 2018. However, it was observed an increase in the goethite (a-FeOOH) spectral area for IB samples, while in the case of PI samples, it was found a ~40% reduction of LC-hematite (α-Fe2O3*). These results can be explained considering the variations in wind speed and direction, temperature increase and the incidence of typical rains from October to March, in the studied region, affecting the contribution from each source to the PI and IB samples. In addition, there is a growing trend of environmental controls of industrial activities located in the region, leading to lower emissions of PM.
Assessment of anthropogenic and natural contributions to the hematite content in the samples
Based on PIXE quantification of iron apportionment and the proportions of anthropogenic iron oxides (having HC-hematite) and natural soil (LC-hematite content) obtained by MS, an estimate of the anthropogenic and natural contributions to the hematite contained in environmental PM samples is calculated using eq.1.
[1]
Where wt%total corresponds to the mass content of iron oxides in the sample either of natural or industrial origin; wt% Fe corresponds to the Fe mass in the sample determined by PIXE, % area corresponds to the relative spectral area of the Fe phases (HC and LC hematite) determined by MS.
Fig. 9 shows the contribution of anthropogenic hematite (α-Fe2O3) and soil hematite (α-Fe2O3*) in the environmental samples studied. Both PI and IB samples exhibit hematite content from both origins being the contribution from iron ore larger than that from soil. IB samples exhibit a much larger contribution from anthropogenic origin (HC-hematite from iron ore) than PI samples. It can be observed that the contribution from soil hematite varies along 2018 and 2019 following similar trends in both collection points, although PI samples exhibits lower apportionments of both iron ore and soil hematite than IB samples.
Application of Chemical mass balance receptor model
The main results of the analytical techniques presented above were used as input data for the EPA-CMB8.2 receptor model (data shown in Fig. 2, and Tables 2 and 6). Some of the elements are present in several sources, such as: Fe, Al, Ca, OC and EC. However, the quantification of EC, OC, highly crystallized and low crystallized hematite allow to significantly reduce the collinearity between sources.
Table 6.
Variation of the iron spectral areas in source samples measured by 57Fe Mössbauer spectroscopy at 25K.
Sources
|
Relative Spectral Area of the Iron Phases
|
α-Fe2O3*
|
α-FeO OH
|
Fe5HO8.4H2O
|
α-Fe2O3
|
Fe1-xS
|
Fe-Ca-Si
|
ɣ-Fe2O3
|
Fe3C
|
Soil
|
1.61
|
2.39
|
1.32
|
0.00
|
0.00
|
0.00
|
0.00
|
0.00
|
Iron Ore
|
0.00
|
4.16
|
0.00
|
47.81
|
0.00
|
0.00
|
0.00
|
0.00
|
Coal
|
0.00
|
0.00
|
0.00
|
0.07
|
0.00
|
0.00
|
0.00
|
0.06
|
Pellets
|
0.00
|
4.26
|
0.00
|
48.43
|
0.00
|
0.00
|
0.00
|
0.00
|
Coke
|
0.00
|
0.00
|
0.00
|
0.00
|
0.48
|
0.00
|
0.50
|
0.00
|
ESLD
|
4.21
|
1.74
|
0.00
|
0.00
|
1.89
|
1.74
|
3.05
|
0.00
|
ESKR
|
1.80
|
0.72
|
0.00
|
0.00
|
2.88
|
0.99
|
0.00
|
0.36
|
CP-AMT
|
0.00
|
2.44
|
0.00
|
10.59
|
0.00
|
0.54
|
0.00
|
0.00
|
VENT2
|
14.04
|
4.05
|
1.35
|
5.67
|
0.00
|
1.62
|
0.00
|
0.00
|
Note: α-Fe2O3* = LC-hematite. α-Fe2O3 = HC-hematite.
Emissions from VENT2 source were found to contain a significant apportionment of α-Fe2O3*, which initially could be related only to soils samples. For this reason, VENT2 source was inserted in the CMB model as part of the CP-AMT. α-Fe2O3* was also found in the ESAF and ESKR sources, most likely due to an isomorphic substitution of Fe3+ in hematite and goethite by another cation, mainly Al, under high process temperatures, reducing or suppressing the Morin transition temperature (Murad et al., 1986; Özdemir et al., 2008).
The presence of Fe-Ca-Si in the environmental PM samples, identified in the MS spectra as (Ca, Fe2+)3(Al, Fe3+)2(SiO4)3, was not balanced by the chemical profile of industrial sources (CP-AMT and IS2), when the EPA-CMB8.2 receptor model was used. This could be explained by a possible variation of this phase in the composition of CP-AMT source, or by the existence of an additional unknown source. The modeling did not accept a Fe-Si-Ca virtual source as an artificial input to balance the phase, indicating that a mixture of Fe-Ca-Si with other phases and chemical elements would be required to improve the solutions.
The results obtained from CMB calculations yield good statistical values assuring that the solutions obtained are reasonably accurate (Tables 7 and 8). T-statistic values were used to determine the statistical significance of the calculation (USEPA Manual, 2004). The lowest T values obtained, “Tmin”, are shown in Table 8. Notice that T > 5 means that the source contribution estimate (SCE) has a relative uncertainty of less than 20%.
Table 7.
Source contributions [%] to environmental samples obtained by application of EPA-CMB8.2 receptor model.
Sources
|
PI1Q18
|
PI2Q18
|
PI3Q18
|
PI1Q19
|
PI2Q19
|
PI3Q19
|
IB1Q18
|
IB2Q18
|
IB3Q18
|
IB1Q19
|
IB2Q19
|
IB3Q19
|
Soil
|
53.4
|
33.1
|
23.9
|
19.8
|
13.2
|
4.0
|
26.0
|
33.1
|
35.5
|
30.8
|
40.7
|
47.6
|
Sea
|
0.0
|
4.3
|
12.6
|
0.6
|
22.9
|
0.3
|
0.1
|
4.3
|
8.0
|
1.2
|
0.7
|
1.6
|
Coal
|
16.5
|
6.4
|
13.1
|
20.6
|
24.2
|
10.5
|
8.9
|
6.4
|
3.8
|
6.0
|
5.9
|
0.3
|
Coke
|
5.4
|
1.1
|
6.1
|
8.5
|
4.9
|
-
|
1.4
|
0.5
|
0.2
|
0.0
|
0.0
|
0.0
|
CP-AMT
|
17.5
|
9.2
|
37.2
|
43.5
|
18.3
|
71.9
|
1.0
|
9.2
|
9.7
|
10.2
|
19.2
|
22.3
|
IS2
|
8.5
|
45.4
|
7.2
|
8.2
|
13.0
|
11.0
|
62.6
|
45.4
|
42.5
|
49.0
|
31.8
|
28.2
|
Table 8
Statistical indicators obtained from the application of EPA-CMB8.2 receptor model to the environmental samples studied.
Index
|
PI1Q18
|
PIQ2Q18
|
PI3Q18
|
PI1Q19
|
PIQ2Q19
|
PI3Q19
|
IB1Q18
|
IB2Q18
|
IB3Q18
|
IB1Q19
|
IB2Q19
|
IB3Q19
|
R2
|
1.00
|
1.00
|
0.99
|
1.00
|
0.99
|
0.99
|
0.99
|
1.00
|
0.99
|
0.99
|
1.00
|
0.98
|
ꭓ2
|
0.33
|
0.57
|
0.77
|
1.25
|
1.24
|
1.31
|
3.51
|
0.81
|
1.59
|
2.06
|
0.56
|
1.67
|
% MASS
|
109.50
|
107.50
|
102.40
|
100.60
|
96.60
|
99.80
|
101.80
|
113.10
|
107.60
|
104.60
|
106.80
|
99.90
|
Tmin
|
5.00
|
11.76
|
11.55
|
9.43
|
9.18
|
10.44
|
12.52
|
5.55
|
6.98
|
10.82
|
12.36
|
9.28
|
Note: % MASS is defined as the percentage given by the ratio of the sum of the SCEs calculated by the model to the total mass concentrations (USEPA Manual, 2004).
Figure 10 shows variations in the balance of the contributions of dust sources present in the region when environmental samples collected in 2018 and 2019 are compared. It is noticeable that the evolution of the apportionment of industrial sources can be tracked down to increases or reductions in their activities. For instance, a major change that occurred in the industrial source (IS2) and observed as a reduction of its contribution in the IB region between the first and third quarter of 2019 (IB1Q19 and IB3Q19) correlates with the change in the supply chain of raw material during that period.
Figure 10 also reveals an asymmetry in the contributions of CP-AMT and IS2 to IB and PI samples, reflecting the fact that IB is closer to IS2 than to AMT, and the opposite for PI. During the third quarter of 2019, it is observed a striking contribution of AMT to the source apportionment of that sample. The contribution of CP-AMT to the particulate material collected in Picanha region during that period has been found to be due to activities involving the relocation of granular materials that took place in CP-AMT yards nearby.
These results show the validity of the application of the EPA-CMB8.2 receptor model, and the effective reduction of source collinearity obtained by Mössbauer Spectroscopy, PIXE and TOT. Such experimental procedure, together with an extensive characterization of the sources to obtain detailed chemical profiles, should help each industrial company to develop effective actions into the appropriate processing activities in order to achieve their environmental objectives. Moreover, it may be used as a systematic monitoring method applied to the prevention of pollution, as well as aid to develop policies towards those goals. As a result of this study, AMT has taken actions to improve environmental control methods and procedures involving granular materials handling operations (e.g. slag, coal, coke), aiming to reduce the impact of this activity on the air quality.