Nitrogen isotope differences between atmospheric nitrate and corresponding nitrogen oxides: A new constraint using oxygen isotopes.

Tracking of reactive nitrogen (N) sources is important for the effective mitigation of N emissions. By combining the N and oxygen (O) isotopes of atmospheric NO3-, stable isotope mixing models were recently applied to evaluate the relative contributions of major NOx sources. However, it has long been unresolved how to accurately constrain the δ15N differences between NO3- and corresponding NOx (ε(NO2→NO3-) values). Here, we first incorporated the HC oxidation (NO2 → NO3-) pathway by using Δ17O values to evaluate the ε(NO2→NO3-) values, performed on NO3- in PM2.5 collected during the day and at night from January 4-13, 2015 at an urban site in Beijing. We found that the Δ17O-based ε values (ε17O-based(NO2→NO3-)) (15.6 ±â€¯7.4‰) differed distinctly from δ18O-based ε values (ε18O-based(NO2→NO3-)) (33.0 ±â€¯9.5‰) so did not properly incorporate the isotopic effects of the HC oxidation (NO2 → NO3-) pathway. Based on the ε(NO2→NO3-) values, δ15N values of NOx from coal combustion (CC), vehicle exhausts (VE), biomass burning (BB), and the microbial N cycle (MC), as well as NO3- in PM2.5, we further quantified the source contributions by using Stable Isotope Analysis in R (the SIAR model). We found that the respective fractional contributions of CC-NOx and MC-NOx were underestimated by 64% and were overestimated by 216% by using ε18O-based(NO2→NO3-) values. We concluded that the new ε17O-based(NO2→NO3-) values reduced uncertainties in contribution analysis and the evaluation method for atmospheric NO3- sources.

Isotopic evaluation on relative contributions of major NOx sources to nitrate of PM2.5 in Beijing.

Nitrate (NO3-) is a key component of secondary inorganic aerosols and PM2.5. However, the contributions of nitrogen oxides (NOx) emission sources to NO3- in PM2.5 remain poorly constrained. This study measured nitrogen (N) isotopes of NO3- (hereafter as δ15N-NO3-) in PM2.5 collected at Beijing in 2014. We observed that δ15N-NO3- values in PM2.5 (-2.3‰â€¯- 19.7‰; 7.3 ±â€¯5.4‰ annually) were significantly higher in winter (11.9 ±â€¯4.4‰) than in summer (2.2 ±â€¯2.5‰). The δ15N differences between source NOx and NO3- in PM2.5 (hereafter as Δ values) were estimated by a computation module as 7.8 ±â€¯2.2‰â€¯- 10.4 ±â€¯1.6‰ (8.8 ±â€¯2.4‰). Using the Δ values and δ15N values of NOx from major fossil (coal combustion, vehicle exhausts) and non-fossil sources (biomass burning, microbial N cycle), contributions of major NOx sources to NO3- in PM2.5 were further estimated by the SIAR model. We found that seasonal variations of δ15N-NO3- values in PM2.5 of Beijing were mainly caused by those of NOx contributions from coal combustion (38 ±â€¯10% in winter, 20 ±â€¯9% in summer). Annually, NOx from coal combustion, vehicle exhausts, biomass burning, and microbial N cycle contributed 28 ±â€¯12%, 29 ±â€¯17%, 27 ±â€¯15%, and 16 ±â€¯7% to NO3- in PM2.5, respectively, showing actually comparable contributions between non-fossil NOx (43 ±â€¯16%) and fossil NOx (57 ±â€¯21%). These results are useful for planning the reduction of NOx emissions in city environments and for elucidating relationships between regional NOx emissions and atmospheric NO3- pollution or deposition.

[Pollution Characteristics Analysis and Risk Assessment of Total Mercury and Methylmercury in Aquatic Products of the Haihe Stem River].

In this study, we analyzed the concentrations of total mercury (THg) and methylmercury (MeNg) in the aquatic products from the Haihe Stem River, and also assessed the risk for the consumers. According to our results, the MeHg and THg concentrations in the aquatic products were 42.51 and 77.31 ng · g⁻¹, respectively (wet weight) . The majority of THg in the aquatic products existed in the form of MeHg (accounting for over 50%). The mercury concentrations varied significantly among different organs in the fish. The BCFs of MeHg for the fish and zoobenthos in the Haihe River were 1.00 x 10 and 4.23 x 104mLg , respectively. Compared with THg, MeHg could accumulate more easily in the aquatic products. Generally, the maximum MeHg and THg concentrations of the aquatic products were much lower than the limit values in China. However, compared with the adults, the MeHg exposure risk for the children was higher, and the THg and MeHg intake could be as high as 154.07 ng (kgd) and 81.11 ng (kg.d)⁻¹, respectively.

Modeled methylmercury exposure and risk from rice consumption for vulnerable populations in a traditional fish-eating area in China.

The circulation of rice from contaminated areas could escalate exposure risk from a local problem to a national issue and affect a wider population beyond the region of origin, as confirmed by the "Poison Rice Incident" in May 2013 in Guangzhou, China. In the present study, the authors established a food chain model based on the aquivalence method to identify major sources of methylmercury (MeHg), estimate the levels of MeHg, and quantify exposure to MeHg via rice and aquatic food consumption. Different types of organism samples from the Haihe River also were collected to verify the calculated values. The MeHg intake in pregnant women was 1529.1 ng/d from the aquatic food chain and as high as 2804.0 ng/d from rice, although the intake varied among scenarios. The maximum possible MeHg concentration in the blood of pregnant women was 5.21 µg/L, higher than the threshold value of MeHg recommended by the US Environmental Protection Agency (4.4 µg/L), which indicated that pregnant women could face risk from MeHg exposure. The authors also assessed the risk of MeHg exposure in pregnant women and their breastfed infants using a new index, HQEquivalent . In 4 scenarios, the HQEquivalent indices ranged from 0.42 to 1.18 for pregnant women and from 0.29 to 0.83 for breastfed infants.