Combining sensor-based measurement and modeling of PM_2.5 and black carbon in assessing exposure to indoor aerosols

Figures & data

Figure 1. Flow chart describing study design.

Figure 2. (a) PM_2.5 values (µg/m³) obtained as MicroPEM™ real-time-averaged values (PM_2.5RT) (n = 36), SKC PM_2.5 (n = 24), MicroPEM™ PM_2.5 (n = 33). (b) Black carbon (BC) values (µg/m³) obtained as microAeth^® averaged real-time values of BC (BC_RT) and SKC BC (n = 23). Horizontal lines in the box plots represent the 10%, 25%, 50%, 75% and 90% percentiles. Wilcoxon signed rank test *p < 0.0167, **p < 0.01, ***p < 0.001.

Table 1. PM_2.5 and black carbon (BC) results.

	MicroPEM™ PM2.5	PM2.5RT	SKC PM2.5	SKC BC	BCRT
	(µg/m^3)	(µg/m^3)	(µg/m^3)	(µg/m^3)	(µg/m^3)
Minimum	2.04	3.30	0.32	<LOD	0.09
Maximum	44.20	77.22	54.35	2.18	2.44
Median	8.89	11.21	7.46	0.25	0.38
Mean	12.67	16.28	12.93	0.49	0.68
Winter mean (n = 7–9)	15.42	22.60	16.11	0.31	0.58
Spring mean (n = 5–7)	11.22	10.56	9.51	0.25	0.36
Fall mean (n = 9–12)	7.71	10.06	10.13	0.62	0.79
Summer mean (n = 1–3)	19.79	29.53	23.04	1.38	1.81

LOD for BC = 0.12 µg/m³, <LOD = LOD/√2.

Table 2. Spearman’s coefficients and Wilcoxon p-values of SKC PM_2.5, MicroPEM™ PM_2.5, PM_2.5RT, SKC BC and BC_RT.

	Spearman’s coefficient		Wilcoxon p-value
Method	SKC rs	MicroPEM™ PM2.5 rs	SKC rs	MicroPEM™ PM2.5
PM2.5RT	PM2.5: 0.77***	0.62***	PM2.5: 0.016*	0.002**
MicroPEM™ PM2.5	PM2.5: 0.74***	–	PM2.5: 0.08	–
BCRT	BC: 0.77***	NA	BC: <0.001***	NA

*p < 0.0167, **p < 0.01,***p < 0.001; NA, not analyzed.

Table 3. Summary statistics for coefficient of variation.

Parameter	Filter	Real-time (RT)	Correction factor (filter/RT)	Coefficient of variation correction Factor	Coefficient of variation linear regression
PM2.5	MicroPEM™ PM2.5 (n = 33)	PM2.5RT	0.94	84%	54% (Equation (3)(3) $$\begin{array}{c}\mathrm{L}\mathrm{N}\left({\text{PM}}_{2.5}\right)=1.125+ \mathrm{L}\mathrm{N}\left(\mathbf{P}{\mathbf{M}}_{\mathbf{2.5}\mathbf{R}\mathbf{T}}\right)*0.5834 +{\beta }_1*0.2269 +{\beta }_2*0.07286 \\ -{\beta }_3*0.1063 –{\beta }_4*0.3587 -{\beta }_5*0.3975–{\mathbf{\beta }}_6*0.0004078 +{\beta }_7*0.00003905\end{array}$$(3) )
PM2.5	SKC PM2.5 (n = 24)	PM2.5RT	0.83	52%	25% (Equation (4)(4) $$\begin{array}{c}\mathrm{L}\mathrm{N}\left({\text{PM}}_{2.5}\right)=-41.49+ \mathrm{L}\mathrm{N}\left(\mathbf{P}{\mathbf{M}}_{\mathbf{2.5}\mathbf{R}\mathbf{T}}\right)*1.428 +{\beta }_1*0.2744 –{\beta }_2*1.700–{\beta }_3*0.08725 +{\mathbf{\beta }}_4*1.902 –{\beta }_8*0.07874 +{\beta }_9*0.7817 +{\beta }_{10}*1.447 -{\beta }_{11}*2.423 –{\beta }_{12}*0.6021 +{\mathbf{\beta }}_{13}*1.149 +{\beta }_{14}*1.472 -{\beta }_6*0.0004267 +{\beta }_{15}*0.0001586 -{\beta }_7*0.00002574 +{\mathbf{\beta }}_{16}*0.01969\end{array}$$(4) )
BC	SKC BC (n = 23)	BCRT	0.74	36%	56% (Equation (5)(5) $$\begin{array}{c}\mathrm{L}\mathrm{N} \left(\mathrm{B}\mathrm{C}\right)=-1.660+ \mathrm{L}\mathrm{N}\left(\mathbf{B}{\mathbf{C}}_{\mathbf{RT}}\right)*0.6920+{\beta }_{12}*0.5493 -{\beta }_{13}*0.5276 +{\beta }_{14}*3.584 -{\beta }_6*0.0003534-{\mathbf{\beta }}_{15}*0.0001418 –{\mathbf{\beta }}_7*0.00007527\end{array}$$(5) )

Table 4. Variables that were included in the multiple regression models with regression estimates (β) and p-values.

	LN MicroPEM™ PM2.5		LN SKC PM2.5		LN SKC BC
	β	p-value	β	p-value	β	p-value
Intercept	1.13	<0.05*	-41.49	<0.05*	–1.66	0.08
LN (PM2.5RT)	0.58	<0.001***	1.43	<0.01**	excl	excl
LN (BCRT)	excl	excl	excl	excl	0.69	<0.01**
Spring	0.23	0.43	0.27	0.72	–	–
Summer	0.07	0.86	–1.70	0.16	–	–
Winter	–0.11	0.70	–0.09	0.82	–	–
Hardwood flooring	–0.36	0.17	1.90	<0.05*	–	–
Cats or dogs present	–	–	0.78	0.11	–	–
Open windows during sampling	–0.40	0.11	–	–	–	–
Kitchen fan during sampling	–	–	–	–	–	–
Frying food during sampling	–	–	1.45	0.05	–	–
Burning candles during sampling	–	–	–2.42	0.08	–	–
Cleaning activities during sampling	–	–	–0.60	0.17	0.55	0.11
HEPA/electrostatic filter in HVAC	–	–	1.15	<0.05*	–0.53	0.30
ECAT score	–	–	1.47	0.51	3.58	0.05
Distance to highway	–0.0004	<0.05*	–0.0004	0.11	–0.0004	0.07
Distance to interstate	–	–	0.0002	0.09	–0.0001	<0.05*
Number of trucks	0.00004	0.10	–0.00003	0.50	–0.00008	<0.05*
People smoking in home	–	–	–0.08	0.92	–	–
Year home built	–	–	0.02	<0.05*	–	–
	R^2 = 0.64		R^2 = 0.94		R^2 = 0.85

**p < 0.01, *p < 0.05;

'–' Not included in model based on the Akaike information criterion;

excl – excluded from regression.