scholarly journals Potential sources and processes affecting speciated atmospheric mercury at Kejimkujik National Park, Canada: comparison of receptor models and data treatment methods

2017 ◽  
Vol 17 (2) ◽  
pp. 1381-1400 ◽  
Author(s):  
Xiaohong Xu ◽  
Yanyin Liao ◽  
Irene Cheng ◽  
Leiming Zhang
Keyword(s):  
Air Pollutants ◽  
National Park ◽  
Treatment Options ◽  
Model Performance ◽  
Principal Component ◽  
Atmospheric Mercury ◽  
Strong Impact ◽  
Data Treatment ◽  
Good For ◽  
Kejimkujik National Park

Abstract. Source apportionment analysis was conducted with positive matrix factorization (PMF) and principal component analysis (PCA) methods using concentrations of speciated mercury (Hg), i.e., gaseous elemental mercury (GEM), gaseous oxidized mercury (GOM), and particulate-bound mercury (PBM), and other air pollutants collected at Kejimkujik National Park, Nova Scotia, Canada, in 2009 and 2010. The results were largely consistent between the 2 years for both methods. The same four source factors were identified in each year using PMF method. In both years, factor photochemistry and re-emission had the largest contributions to atmospheric Hg, while the contributions of combustion emission and industrial sulfur varied slightly between the 2 years. Four components were extracted with air pollutants only in each year using PCA method. Consistencies between the results of PMF and PCA include (1) most or all PMF factors overlapped with PCA components, (2) both methods suggest strong impact of photochemistry but little association between ambient Hg and sea salt, and (3) shifting of PMF source profiles and source contributions from one year to another was echoed in PCA. Inclusion of meteorological parameters led to identification of an additional component, Hg wet deposition in PCA, while it did not affect the identification of other components. The PMF model performance was comparable in 2009 and 2010. Among the three Hg forms, the agreements between model-reproduced and observed annual mean concentrations were excellent for GEM, very good for PBM, and acceptable for GOM. However, on a daily basis, the agreement was very good for GEM but poor for GOM and PBM. Sensitivity tests suggest that increasing sample size by imputation is not effective in improving model performance, while reducing the fraction of concentrations below method detection limit, by either scaling GOM and PBM to higher concentrations or combining them to reactive mercury, is effective. Most of the data treatment options considered had little impact on the source identification or contribution.

scholarly journals Potential sources and processes affecting speciated atmospheric mercury at Kejimkujik National Park, Canada

2016 ◽  
Author(s):  
Xiaohong Xu ◽  
Yanying Liao ◽  
Irene Cheng ◽  
Leiming Zhang
Keyword(s):  
Air Pollutants ◽  
National Park ◽  
Treatment Options ◽  
Model Performance ◽  
Principal Component ◽  
Elemental Mercury ◽  
Atmospheric Mercury ◽  
Strong Impact ◽  
Good For ◽  
Kejimkujik National Park

Abstract. Source apportionment analysis was conducted with Positive Matrix Factorization (PMF) and Principal Component Analysis (PCA) methods using concentrations of speciated mercury (Hg), i.e., gaseous elemental mercury (GEM), gaseous oxidized mercury (GOM), and particulate-bound mercury (PBM), and other air pollutants collected at Kejimkujik National Park, Nova Scotia, Canada in 2009 and 2010. The results were largely consistent between the two years for both methods. The same four source factors were identified in each year using PMF method. In both years, factor Photochemistry and Re-emission had the largest contributions to atmospheric Hg, while the contributions of Combustion Emission and Industrial Sulfur varied slightly between the two years. Four components were extracted with air pollutants only in each year using PCA method. Consistency between the results of PMF and PCA include, 1) most or all PMF factors overlapped with PCA components, 2) both methods suggest strong impact of photochemistry, but little association between ambient Hg and sea salt, 3) shifting of PMF source profiles and source contributions from one year to another was echoed in PCA. Inclusion of meteorological parameters led to identification of an additional component – Hg Wet Deposition in PCA, while it did not affect the identification of other components. The PMF model performance was comparable in 2009 and 2010. Among the three Hg forms, the agreement between predicted and observed annual mean concentrations were excellent for GEM, very good for PBM and acceptable for GOM. However, on daily basis, the agreement was very good for GEM, but poor for GOM and PBM. Sensitivity tests suggest that increasing sample size by imputation is not effective in improving model performance, while reducing the fraction of concentrations below method detection limit, by either scaling GOM and PBM to higher concentrations or combining them to reactive mercury, is effective. Most of the data treatment options considered had little impact on the source identification/contribution.

scholarly journals Ensemble Predictions of Air Pollutants in China in 2013 for Health Effects Studies Using WRF/CMAQ Modeling System with Four Emission Inventories

2017 ◽  
Author(s):  
Jianlin Hu ◽  
Xun Li ◽  
Lin Huang ◽  
Qi Ying ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Air Quality ◽  
Health Effects ◽  
Air Pollutants ◽  
Emission Inventory ◽  
Model Performance ◽  
Observation Data ◽  
Emission Inventories ◽  
Air Quality Model ◽  
Quality Model ◽  
Future Health

Abstract. Accurate exposure estimates are required for health effects analyses of severe air pollution in China. Chemical transport models (CTMs) are widely used tools to provide detailed information of spatial distribution, chemical composition, particle size fractions, and source origins of pollutants. The accuracy of CTMs' predictions in China is largely affected by the uncertainties of public available emission inventories. The Community Multi-scale Air Quality model (CMAQ) with meteorological inputs from the Weather Research and Forecasting model (WRF) were used in this study to simulate air quality in China in 2013. Four sets of simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC), the Emission Inventory for China by School of Environment at Tsinghua University (SOE), the Emissions Database for Global Atmospheric Research (EDGAR), and the Regional Emission inventory in Asia version 2 (REAS2). Model performance was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O3 and PM2.5 with the four inventories generally meet the criteria of model performance, but difference exists in different pollutants and different regions among the inventories. Ensemble predictions were calculated by linearly combining the results from different inventories under the constraint that sum of the squared errors between the ensemble results and the observations from all the cities was minimized. The ensemble annual concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB) and mean fractional errors (MFE) of the ensemble predicted annual PM2.5 at the 60 cities are −0.11 and 0.24, respectively, which are better than the MFB (−0.25–−0.16) and MFE (0.26–0.31) of individual simulations. The ensemble annual 1-hour peak O3 (O3-1 h) concentrations are also improved, with mean normalized bias (MNB) of 0.03 and mean normalized errors (MNE) of 0.14, compared to MNB of 0.06–0.19 and MNE of 0.16–0.22 of the individual predictions. The ensemble predictions agree better with observations with daily, monthly, and annual averaging times in all regions of China for both PM2.5 and O3-1 h. The study demonstrates that ensemble predictions by combining predictions from individual emission inventories can improve the accuracy of predicted temporal and spatial distributions of air pollutants. This study is the first ensemble model study in China using multiple emission inventories and the results are publicly available for future health effects studies.

Are Methylmercury Concentrations in the Wetlands of Kejimkujik National Park, Nova Scotia, Canada, Dependent on Geology?

2003 ◽  
Vol 32 (6) ◽  
pp. 2085-2094 ◽  
Author(s):  
Steven D. Siciliano ◽  
Al Sangster ◽  
Chris J. Daughney ◽  
Lisa Loseto ◽  
James J. Germida ◽  
...  
Keyword(s):  
Nova Scotia ◽  
National Park ◽  
Kejimkujik National Park

scholarly journals 33 Aplicação da análise exploratória de dados na discriminação geográfica do quiabo do Rio Grande do Norte e Pernambuco

2018 ◽  
Vol 34 (3) ◽  
pp. 33
Author(s):  
Francisco Dos Santos Panero ◽  
Maria de Fátima Pereira Vieira ◽  
Ângela Maria Paiva Cruz ◽  
Maria de Fátima Vitória De Moura ◽  
Henrique Eduardo Bezerra Da Silva
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Component ◽  
Rio Grande ◽  
Component Analysis ◽  
The State ◽  
Data Treatment ◽  
Different Origins

Samples of okra from Caruaru and Vitória of Santo Antão, in the State of Pernambuco, and Ceará-Mirim, Macaíba and Extremoz in the State of Rio Grande do Norte have been analysed. Two different methods were applied in the data treatment allowing to geographically discriminate samples from different origins: Principal Component Analysis - PCA and Hierarquical Cluster Analysis - HCA.

scholarly journals Arthropod community at different altitudes in Gunung Halimun-Salak National Park, Western Java, Indonesia

2019 ◽  
Vol 20 (6) ◽  
Author(s):  
NOOR FARIKHAH HANEDA ◽  
IWAN HILWAN ◽  
EWI IRFANI
Keyword(s):  
National Park ◽  
Pearson Correlation ◽  
Principal Component ◽  
Environmental Parameters ◽  
Soil Arthropods ◽  
Arthropod Community ◽  
Total N ◽  
Organic C ◽  
Soil Arthropod ◽  
Different Altitudes

Abstract. Haneda NF, Hilwan I, Irfani E. 2019. Arthropod community at different altitudes in Gunung Halimun-Salak National Park, Western Java, Indonesia. Biodiversitas 20: 1735-1742. Gunung Halimun Salak National Park (GHSNP) stores high biodiversity both from its flora and fauna. Parts of the diversity that have not been widely explored are soil arthropods at different altitudes. The aim of this study was to analyze soil arthropod community and the correlation between the attributes of soil arthropods and the environmental factors. The soil arthropods were collected using pitfall traps, placed in several altitudes, i.e., 500 m, 700 m, 900 m, 1100 m, 1300 m, 1500 m, and 1700 m . The attributes of community and environmental parameters were analyzed using Pearson correlation and principal component analysis. The result showed that family Formicidae dominated the soil arthropod community. The diversity of arthropods increased with increasing altitudes. The habitat at the altitudes of 1500 m, 1300 m and 1100 m had a dense canopy, thick litter and high total N and organic C. There was positive correlation between the attributes of soil arthropod community and environment variables.

scholarly journals Evaluating skill of BMKG wave model forecast (Wavewatch-3) with observation data in Indian Ocean (5 – 31 December 2017).

2021 ◽  
Vol 893 (1) ◽  
pp. 012058
Author(s):  
R Kurniawan ◽  
H Harsa ◽  
A Ramdhani ◽  
W Fitria ◽  
D Rahmawati ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Wave Height ◽  
Model Performance ◽  
Forecast Accuracy ◽  
Wave Model ◽  
Ocean Waves ◽  
Observation Data ◽  
Operating Characteristics ◽  
Wave Forecast ◽  
Good For

Abstract Providing Maritime meteorological forecasts (including ocean wave information) is one of BMKG duties. Currently, BMKG employs Wavewatch-3 (WW3) model to forecast ocean waves in Indonesia. Evaluating the wave forecasts is very important to improve the forecasts skill. This paper presents the evaluation of 7-days ahead BMKG’s wave forecast. The evaluation was performed by comparing wave data observation and BMKG wave forecast. The observation data were obtained from RV Mirai 1708 cruise on December 5th to 31st 2017 at the Indian Ocean around 04°14'S and 101°31'E. Some statistical properties and Relative Operating Characteristics (ROC) curve were utilized to assess the model performance. The evaluation processes were carried out on model’s parameters: Significant Wave Height (Hs) and Wind surface for each 7-days forecast started from 00 UTC. The comparation results show that, in average, WW3 forecasts are over-estimate the wave height than that of the observation. The forecast skills determined from the correlation and ROC curves are good for the first- and second-day forecast, while the third until seventh day decrease to fair. This phenomenon is suspected to be caused by the wind data characteristics provided by the Global Forecasts System (GFS) as the input of the model. Nevertheless, although statistical correlation is good for up to 2 days forecast, the average value of Root Mean Square Error (RMSE), absolute bias, and relative error are high. In general, this verifies the overestimate results of the model output and should be taken into consideration to improve BMKG’s wave model performance and forecast accuracy.

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