Source Apportionment of Ambient PM_(10) in the Urban Area of Longyan City,China:a Comparative Study Based on Chemical Mass Balance Model and Factor Analysis Method

In order to identify the day and night pollution sources of PM10 in ambient air in Longyan City,the authors analyzed the elemental composition of respirable particulate matters in the day and night ambient air samples and various pollution sources which were collected in January 2010 in Longyan with inductivity coupled plasma-mass spectrometry（ICP-MS）.Then chemical mass balance（CMB） model and factor analysis（FA） method were applied to comparatively study the inorganic components in the sources and receptor samples.The results of factor analysis show that the major sources were road dust,waste incineration and mixed sources which contained automobile exhaust,soil dust/secondary dust and coal dust during the daytime in Longyan City,China.There are two major sources of pollution which are soil dust and mixture sources of automobile exhaust and secondary dust during the night in Longyan.The results of CMB show that the major sources are secondary dust,automobile exhaust and road dust during the daytime in Longyan.The major sources are secondary dust,soil dust and automobile exhaust during the night in Longyan.The results of the two methods are similar to each other and the results will guide us to plan to control the PM10 pollution sources in Longyan.

Test of newly developed conceptual hydrological model for simulation of rain-on-snow events in forested watershed

A conceptual hydrological model that links the Xin＇anjiang hydrological model and a physically based snow energy and mass balance model, described as the XINSNOBAL model, was developed in this study for simulating rain-on-snow events that commonly occur in the Pacific Northwest of the United States. The resultant model was applied to the Lookout Creek Watershed in the H. J. Andrews Experimental Forest in the western Cascade Mountains of Oregon, and its ability to simulate streamflow was evaluated. The simulation was conducted at 24-hour and one-hour time scales for the period of 1996 to 2005. The results indicated that runoffand peak discharge could be underestimated if snowpack accumulation and snowmelt under rain-on-snow conditions were not taken into account. The average deterministic coefficient of the hourly model in streamflow simulation in the calibration stage was 0.837, which was significantly improved over the value of 0.762 when the Xin＇anjiang model was used alone. Good simulation performance of the XINSNOBAL model in the WS 10 catchment, using the calibrated parameter of the Lookout Creek Watershed for proxy-basin testing, demonstrates that transplanting model parameters between similar watersheds can orovide a useful tool for discharge forecastin~, in un^au^ed basins.

Modeling historical budget for β-Hexachlorocyclohexane(HCH)in the Arctic Ocean:A contrast to α-HCH

The historical annual loading to,removal from,and cumulative burden in the Arctic Ocean for β-hexachlorocyclohexane(β-HCH),an isomer comprising 5e12%of technical HCH,is investigated using a mass balance box model from 1945 to 2020.Over the 76 years,loading occurred predominantly through ocean currents and river inflow(83%)and only a small portion via atmospheric transport(16%).β-HCH started to accumulate in the Arctic Ocean in the late 1940s,reached a peak of 810 t in 1986,and decreased to 87 t in 2020,when its concentrations in the Arctic water and air were~30 ng m^(-3)and~0.02 pg m^(-3),respectively.Even though β-HCH and α-HCH(60e70%of technical HCH)are both the isomers of HCHs with almost identical temporal and spatial emission patterns,these two chemicals have shown different major pathways entering the Arctic.Different from α-HCH with the long-range atmospheric transport(LRAT)as its major transport pathway,β-HCH reached the Arctic mainly through long-range oceanic transport(LROT).The much higher tendency of β-HCH to partition into the water,mainly due to its much lower Henry's Law Constant than α-HCH,produced an exceptionally strong pathway divergence with β-HCH favoring slow transport in water and α-HCH favoring rapid transport in air.The concentration and burden of β-HCH in the Arctic Ocean are also predicted for the year 2050 when only 4.4-5.3 t will remain in the Arctic Ocean under the influence of climate change.

Chemical speciation and anthropogenic sources of ambient volatile organic compounds (VOCs) during summer in Beijing, 2004

Volatile organic compounds(VOCs)were measured at six sites in Beijing in August,2004.Up to 148 VOC species,including C_(3) to C_(12) alkanes,C_(3) to C_(11) alkenes,C_(6) to C_(12) aromatics,and halogenated hydrocarbons,were quantified.Although the concentrations differed at the sites,the chemical compositions were similar,except for the Tongzhou site where aromatics were significantly high in the air.Based on the source profiles measured from previous studies,the source apportionment of ambient VOCs was preformed by deploying the chemical mass balance(CMB)model.The results show that urban VOCs are predominant from mobile source emissions,which contribute more than 50% of the VOCs(in mass concentrations)to ambient air at most sites.Other important sources are gasoline evaporation,painting,and solvents.The exception is at the Tongzhou site where vehicle exhaust,painting,and solvents have about equal contribution,around 35% of the ambient VOC concen-tration.As the receptor model is not valid for deriving the sources of reactive species,such as isoprene and 1,3-butadiene,other methodologies need to be further explored.

TSP aerosol source apportionment in the urban region of the Indian steel city,Rourkela

Steel industries are a major contributor to aerosols in steel cities like Rourkela. We designed an air quality sampling program to characterize total suspended particulate （TSP） aerosol in urban areas of Rourkela and to identify their steel-related and other sources. Monitoring was carried out over 8 h, twice per week from January 2011 to December 2012. Metallic species of TSP aerosols were analyzed using an atomic absorption spectrophotometer; ionic species using the IS 3025 method; and carbonaceous species using a total organic carbon analyzer. Enrichment factor and Spearman＇s rank correlation analysis were carried out on compositional data. Significant seasonal variations were observed for TSP with totals in sum- mer 〉 spring 〉 winter 〉 monsoon. Low concentrations during monsoon reflected wet scavenging, while high concentrations during summer were related to wind turbulence and low humidity. The chemical mass balance model CMB8.2 was applied to apportion sources. Particles related to steel production, road dust, and soil were dominant in all seasons. A fertilizer plant was found to contribute particles in summer and monsoon. Wood combustion, diesel exhaust, and liquefied petroleum gas contributed significantly in spring and winter. While diesel exhaust, industrial manufacturing, solid waste burning, cement kilns, and construction were found to contribute to TSP at various times throughout the year.