Examining trends and variability of PM2.5-associated organic and elemental carbon in the megacity of Beijing, China: Insight from decadal continuous in-situ hourly observations.

Organic carbon (OC) and elemental carbon (EC) in fine particulate matter (PM2.5) play pivotal roles in impacting human health, air quality, and climate change dynamics. Long-term monitoring datasets of OC and EC in PM2.5 are indispensable for comprehending their temporal variations, spatial distribution, evolutionary patterns, and trends, as well as for assessing the effectiveness of clean air action plans. This study presents and scrutinizes a comprehensive 10-year hourly dataset of PM2.5-bound OC and EC in the megacity of Beijing, China, spanning from 2013 to 2022. Throughout the entire study period, the average concentrations of OC and EC were recorded at 8.8 ± 8.7 and 2.5 ± 3.0 µg/m3, respectively. Employing the seasonal and trend decomposition methodology, specifically the locally estimated scatter plot smoothing method combined with generalized least squares with the autoregressive moving average method, the study observed a significant decline in OC and EC concentrations, reducing by 5.8 % yr-1 and 9.9 % yr-1 at rates of 0.8 and 0.4 µg/m3 yr-1, respectively. These declining trends were consistently verified using Theil-Sen method. Notably, the winter months exhibited the most substantial declining trends, with rates of 9.3 % yr-1 for OC and 10.9 % yr-1 for EC, aligning with the positive impact of the implemented clean air action plan. Weekend spikes in OC and EC levels were attributed to factors such as traffic regulations and residential emissions. Diurnal variations showcased higher concentrations during nighttime and lower levels during daytime. Although meteorological factors demonstrated an overall positive impact with average reduction in OC and EC concentrations by 8.3 % and 8.7 %, clean air action plans including the Air Pollution Prevention and Control Action Plan (2013-2017) and the Three-Year Action Plan to Win the Blue Sky War (2018-2020) have more contributions in reducing the OC and EC concentrations with mass drop rates of 87.1 % and 89.2 % and 76.7 % and 96.7 %, respectively. Utilizing the non-parametric wind regression method, significant concentration hotspots were identified at wind speeds of ≤2 m/s, with diffuse signals recorded in the southwestern wind sectors at wind speeds of approximately 4-5 m/s. Interannual disparities in potential source regions of OC and EC were evident, with high potential source areas observed in the southern and northwestern provinces of Beijing from 2013 to 2018. In contrast, during 2019-2022, potential source areas with relatively high values of potential source contribution function were predominantly situated in the southern regions of Beijing. This analysis, grounded in observational data, provides insights into the decadal changes in the major atmospheric composition of PM2.5 and facilitates the evaluation of the efficacy of control policies, particularly relevant for developing countries.

An approach for carbon content measurement in marine sediment: Application of organic and elemental carbon analyzer.

Organic and Elemental Carbon (OEC) is widely applied in the atmospheric sciences for determining carbon content and distinguishing black carbon contents of aerosols, with an advantage that OEC-based approach can provide thermograms derived from carbonaceous material. It is potential to adopt the advantage to measure the content and composition of organic carbon (OC)% in marine sediments. Here, we utilized the OEC analyzer to measure the OC% in marine sediment based on the pyrolytic oxidation principle, and obtain the OC-derived carbon dioxide (CO2) thermograms. We examined marine sediments and reference materials to understand the stability and reproducibility of OC% measurements using our approach. The findings indicate that the OC% results (ranging from 1.44 to 1.59%, ave. 1.55 ± 0.03%, n = 64) based on this approach are accurate. In addition, CO2 concentration thermograms obtained by repeated measurements exhibit a strong reproducibility. Our approach can thus provide the concentrations of thermally-evolved CO2 with increasing heating temperature to deeply understand the reactivities of OC and the compositions in sediments. We suggest that the OEC-based OC% measurement is credible when samples preparation is well-performed (e.g., suitable sample mass and uniformly distributed loading). To sum up, we provide a means to accurately determine the OC% in marine sediments in terms of the ramped-pyrolysis principle.

New insights into submicron particles impact on visibility.

The aim of the study was to analyze the impact of very fine atmospheric particles (submicron particulate matter; PM1) on visibility deterioration. Taking into consideration not only their entirely different physio-chemical properties in comparison to a well-recognized PM10 but also the origin and a growing environmental awareness of PM1, the main research problem has been solved in few steps. At first, the chemical composition of PM1 was determined in two selected urban areas in Poland. Measurements of meteorological parameters, i.e., air temperature and humidity, precipitation, atmospheric pressure, wind speed, and visibility, were also conducted. The next step of the work was the analysis of (1) seasonal changes of the concentration of PM1 and its main components, (2) the influence of chemical components of PM1 on light extinction, and (3) the influence of PM1 and humidity on visibility. Hierarchical cluster analysis, correlation matrixes and a heat map, and classification and regression tree analysis were used. The light extinction coefficient is influenced mainly by coarse mass of PM, and PM1-bound ammonium nitrate, organic matter, and by Rayleigh scattering. The less important in the light extinction coefficient shaping has PM1-bound ammonium sulfate, elemental carbon, and soil. In this way, the secondary origin PM1 components were proved to most significantly influence the visibility. The obtained results confirmed the possibility of the use of statistical agglomeration techniques to identify ranges of variation of visibility, including independent variables adopted to analyses (meteorological conditions, chemical composition of PM1, etc.).