Chemical characterisation and source apportionment of PM₁ during massive loading at an urban location in Indo-Gangetic Plain: impact of local sources and long-range transport

Figures & data

Table 1. Analytical result on chemical constituents in PM₁ (this study)

Species	(ng m^−3)	Avg±SD(µg m^−3)	(%)
PM1	500	113±72	10
	17	12.5±14.3	14
Cl^−	3	2.0±2.3	20
	8.7	4.3±5.4	20
	12	12.5±9.2	14
Na	0.11	2.7±2.0	22
K	0.11	1.4±1.1	22
Ca	0.11	4.1±3.7	22
Fe	6.25	2.6±2.0	10
Mg	0.1	0.8±0.7	10
		(ng m^−3)
As	2.12	0.4±2.8	10
Cd	1.33	42.2±239.8	10
Co	2.08	0.1±0.5	10
Cr	4.13	357.8±436.2	10
Cu	10.42	61.1±152.4	10
Ni	2.08	179.1±375.9	10
Pb	22.928	913.2±2874.6	10
Se	2.08	29.0±38.8	10
V	2.08	244.1±401.6	10
Zn	2.08	994.5±3502.9	10
Mn	2.08	35.3±88.5	10

^aDerived from 3σ of the blank.

^bAdopted and modified after (Mehta et al., 2009).

Fig. 1 Temporal variability of: (a) PM₁; (b, c) mass fractions of metals and water-soluble inorganic species (WSIS) and (d) K (fine-mode) mass concentration.

Fig. 2 Temporal variability of: (a) ammonium-to-sulphate equivalent ratio and (b) nitrate-to-sulphate mass ratio.

Fig. 3 Spatial variability in chemical composition of WSIS (here SWSIS=

+

+

+ Cl⁻) in PM₁ from some of the urban, rural, coastal and high-altitude locations. Also shown is the nitrate-to-sulphate mass ratio for these locations. Mass concentration of ∑WSIS [given in brackets] is in µg m⁻³.

Fig. 4 PMF analysis showing: (a) Species labelled as strong and weak (arbitrary scale on Y-axis) based on residual and regression analyses. Species with asterisk (As and Co) represent the bad species. (b) Variability of Q-Robust as a function of run number.

Fig. 5 PMF-based percentage contribution of resolved factors in PM₁.

Fig. 6 Percentage contribution of resolved sources in conjunction with air-mass back trajectories (5 d; 1000 m above mean sea level) on sampling days at Kanpur (shown by a circle) shows the predominance of: (a) coal combustion; (b) leather tanneries; (c) industrial emission and construction activities; (d) diesel emission; (e) secondary aerosols and (f) biomass burning emissions.

Mehta B. , Venkataraman C. , Bhushan M. , Tripathi S. N . Identification of sources affecting fog formation using receptor modeling approaches and inventory estimates of sectoral emissions. Atmos. Environ. 2009; 43: 1288–1295.

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