Implications of variations in stream specific conductivity for estimating baseflow using chemical mass balance and calibrated hydrograph techniques

Baseflow to rivers comprises regional groundwater and lower-salinity intermediate water stores such as interflow, soil water, and bank return flows. Chemical mass balance (CMB) calculations based on the specific conductivity (SC) of rivers potentially estimate the groundwater contribution to baseflow. This study discusses the application of the CMB approach in rivers from south-eastern Australia and assesses the feasibility of calibrating recursive digital filters (RDFs) and sliding minima (SM) techniques based on streamflow data to estimate groundwater inflows. The common strategy of assigning the SC of groundwater inflows based on the highest annual river SC may not always be valid due to the persistent presence of lower-salinity intermediate waters. Rather, using the river SC from low-flow periods during drought years may be more realistic. If that is the case, the estimated groundwater inflows may be lower than expected, which has implications for assessing contaminant transport and the impacts of near-river groundwater extraction. Probably due to long-term variations in the proportion of groundwater in baseflow, the RDF and SM techniques cannot generally be calibrated using the CMB results to estimate annual baseflow proportions. Thus, it is not possible to extend the estimates of groundwater inflows using those methods, although in some catchments reasonable estimates of groundwater inflows can be made from annual streamflows. Short-term variations in the composition of baseflow also lead to baseflow estimates made using the CMB method being far more irregular than expected. This study illustrates that estimating baseflow, especially groundwater inflows, is not straightforward.

Surface Water Quality Assessment of Panchagnaga River and Development of DO-BOD Relationship Using Empirical Approach

Surface water samples were collected from selected locations along river Panchaganga, from Kolhapur to Narsobawadi during April 2019. Physicochemical parameters were determined in the laboratory and chemical mass balance approach was adopted to estimate the individual ionic loads in the river water. Streeter-Phelps equation was applied to derive a relationship between DO and BOD5. Model parameters such as De-oxygenation Rate (Kd) and Re-aeration Rates (Kr ) were optimized using different empirical methods. The result of chemical mass balance showed an increase in the loading of various ions from upstream to downstream which could be attributed to agricultural and industrial wastes that enter the main stream. De-oxygenation rate and re-aeration constants were calculated using various empirical methods. DO sag curve was developed using Streeter Phelp’s model and compared with the observed parameters which showed a significant correlation. DO-BOD concentration observed along the course of the river indicated that the self-purification capacity of the river is high due to which the river regains the lost DO level at a distance less than 50 meters.

Implications of variations in stream specific conductivity for estimating baseflow using chemical mass balance and calibrated hydrograph techniques

Baseflow to rivers comprises regional groundwater and lower salinity intermediate water stores such as interflow, soil water, and bank return flows. Chemical mass balance (CMB) calculations based on the specific conductivity (SC) of rivers potentially estimates the groundwater contribution to baseflow. This study discusses the application of the CMB approach in rivers from southeast Australia and assesses the feasibility of calibrating recursive digital filters (RDF) and sliding minima (SM) techniques based on streamflow data to estimate groundwater inflows. The common strategy of assigning the SC of groundwater inflows based on the highest annual river SC may not always be valid due to the long-term presence of lower salinity intermediate waters. Rather, using the river SC from low flow periods during drought years may be more realistic. If that is the case, the estimated groundwater inflows may be lower than expected, which has implications for assessing contaminant transport and the impacts of near-river groundwater extraction. Probably due to long-term variations in the proportion of groundwater in baseflow, the RDF and SM techniques cannot generally be calibrated using the CMB results to estimate annual baseflow proportions. Thus, it is not possible to extend the estimates of groundwater inflows using those methods, although in some catchments reasonable estimates of groundwater inflows can be made from annual streamflows. Short-term variations in the composition of baseflow also leads to baseflow estimates made using the CMB method being far more irregular than expected. This study illustrates that estimating baseflow, especially groundwater inflows, is not straightforward.

Emission of PM2.5-Bound Polycyclic Aromatic Hydrocarbons from Biomass and Coal Combustion in China

Field measured PAH emissions from diverse sources in China are limited or even not available. In this study, the PM2.5-bound PAH emission factors (EFs) for typical biomass and coal combustion in China were determined on-site. The measured total PAH EFs were 24.5 mg/kg for household coal burning, 10.5–13.9 mg/kg for household biofuel burning, 8.1–8.6 mg/kg for biomass open burning, and 0.021–0.31 mg/kg for coal-fired boilers, respectively. These EF values were compared with previous studies. The sources profiles of PAHs for four sources were developed to use in chemical mass balance receptor modelling. BaP equivalent EFs (EFBaPeq) were calculated to evaluate PAH emission toxicity among different combustion sources, and were 6.81, 2.94–4.22, 1.59–3.62, and 0.0006–0.042 mg/kg for those four types of sources, respectively.

Source apportionment of fine organic carbon at an urban site of Beijing using a chemical mass balance model

Fine particles were sampled from 9 November to 11 December 2016 and 22 May to 24 June 2017 as part of the Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-China) field campaigns in urban Beijing, China. Inorganic ions, trace elements, organic carbon (OC), elemental carbon (EC), and organic compounds, including biomarkers, hopanes, polycyclic aromatic hydrocarbons (PAHs), n-alkanes, and fatty acids, were determined for source apportionment in this study. Carbonaceous components contributed on average 47.2 % and 35.2 % of total reconstructed PM2.5 during the winter and summer campaigns, respectively. Secondary inorganic ions (sulfate, nitrate, ammonium; SNA) accounted for 35.0 % and 45.2 % of total PM2.5 in winter and summer. Other components including inorganic ions (K+, Na+, Cl−), geological minerals, and trace metals only contributed 13.2 % and 12.4 % of PM2.5 during the winter and summer campaigns. Fine OC was explained by seven primary sources (industrial and residential coal burning, biomass burning, gasoline and diesel vehicles, cooking, and vegetative detritus) based on a chemical mass balance (CMB) receptor model. It explained an average of 75.7 % and 56.1 % of fine OC in winter and summer, respectively. Other (unexplained) OC was compared with the secondary OC (SOC) estimated by the EC-tracer method, with correlation coefficients (R2) of 0.58 and 0.73 and slopes of 1.16 and 0.80 in winter and summer, respectively. This suggests that the unexplained OC by the CMB model was mostly associated with SOC. PM2.5 apportioned by the CMB model showed that the SNA and secondary organic matter were the two highest contributors to PM2.5. After these, coal combustion and biomass burning were also significant sources of PM2.5 in winter. The CMB results were also compared with results from the positive matrix factorization (PMF) analysis of co-located aerosol mass spectrometer (AMS) data. The CMB model was found to resolve more primary organic aerosol (OA) sources than AMS-PMF, but the latter could apportion secondary OA sources. The AMS-PMF results for major components, such as coal combustion OC and oxidized OC, correlated well with the results from the CMB model. However, discrepancies and poor agreements were found for other OC sources, such as biomass burning and cooking, some of which were not identified in AMS-PMF factors.