Spatio-temporal trends of heavy metals and source apportionment in Tolo Harbour, Hong Kong

2009 ◽  
Vol 60 (7) ◽  
pp. 1439-1445 ◽  
Author(s):  
Kouping Chen ◽  
Jiu Jimmy Jiao
Keyword(s):  
Heavy Metals ◽  
Hong Kong ◽  
Source Apportionment ◽  
Temporal Trends ◽  
Tolo Harbour ◽  
Spatio Temporal

scholarly journals Macrobenthic Community in Tolo Harbour, Hong Kong and its Relations with Heavy Metals

2010 ◽  
Vol 33 (3) ◽  
pp. 600-608 ◽  
Author(s):  
Kouping Chen ◽  
Shengyan Tian ◽  
Jiu Jimmy Jiao
Keyword(s):  
Heavy Metals ◽  
Hong Kong ◽  
Macrobenthic Community ◽  
Tolo Harbour

scholarly journals Long-Term Spatio-Temporal Trends of Organotin Contaminations in the Marine Environment of Hong Kong

PLoS ONE ◽  
2016 ◽  
Vol 11 (5) ◽  
pp. e0155632 ◽  
Author(s):  
Kevin K. Y. Ho ◽  
Guang-Jie Zhou ◽  
Elvis G. B. Xu ◽  
Xinhong Wang ◽  
Kenneth M. Y. Leung
Keyword(s):  
Hong Kong ◽  
Marine Environment ◽  
Temporal Trends ◽  
Spatio Temporal

Spatio-temporal trends and source apportionment of fossil fuel and biomass burning black carbon (BC) in the Los Angeles Basin

2018 ◽  
Vol 640-641 ◽  
pp. 1231-1240 ◽  
Author(s):  
Amirhosein Mousavi ◽  
Mohammad H. Sowlat ◽  
Sina Hasheminassab ◽  
Andrea Polidori ◽  
Constantinos Sioutas
Keyword(s):  
Los Angeles ◽  
Black Carbon ◽  
Source Apportionment ◽  
Biomass Burning ◽  
Fossil Fuel ◽  
Temporal Trends ◽  
Los Angeles Basin ◽  
Spatio Temporal

Spatio-temporal patterns and source apportionment of coastal water pollution in eastern Hong Kong

2007 ◽  
Vol 41 (15) ◽  
pp. 3429-3439 ◽  
Author(s):  
Feng Zhou ◽  
Gordon H. Huang ◽  
Huaicheng Guo ◽  
Wei Zhang ◽  
Zejia Hao
Keyword(s):  
Water Pollution ◽  
Hong Kong ◽  
Source Apportionment ◽  
Coastal Water ◽  
Temporal Patterns ◽  
Spatio Temporal

scholarly journals Spatio-Temporal Analysis of Heavy Metals in Arid Soils at the Catchment Scale Using Digital Soil Assessment and a Random Forest Model

2021 ◽  
Vol 13 (9) ◽  
pp. 1698
Author(s):  
Ruhollah Taghizadeh-Mehrjardi ◽  
Hassan Fathizad ◽  
Mohammad Ali Hakimzadeh Ardakani ◽  
Hamid Sodaiezadeh ◽  
Ruth Kerry ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Random Forest ◽  
Metal Contamination ◽  
Heavy Metal Contamination ◽  
Temporal Trends ◽  
Temporal Analysis ◽  
Catchment Scale ◽  
Spatio Temporal ◽  
The Impact

Predicting the spatio-temporal distribution of absorbable heavy metals in soil is needed to identify the potential contaminant sources and develop appropriate management plans to control these hazardous pollutants. Therefore, our aim was to develop a model to predict soil adsorbable heavy metals in arid regions of Iran from 1986 to 2016. Soil adsorbable heavy metals were measured in 201 samples from locations selected using the Latin hypercube sampling method in 2016. A random forest (RF) model was used to determine the relationship between a suite of geospatial predictors derived from remote sensing and digital elevation model data with georeferenced measurements of soil absorbable heavy metals. The trained RF model from 2016 was used to reconstruct the spatial distribution of soil absorbable heavy metals at three historical timesteps (1986, 1999, and 2010). Results indicated that the RF model was effective at predicting the distribution of heavy metals with coefficients of determination of 0.53, 0.59, 0.41, 0.45, and 0.60 for Fe, Mn, Ni, Pb, and Zn, respectively. The predicted maps showed high spatio-temporal variability; for example, there were substantial increases in Pb (the 1.5–2 mg/kg−1 class) where its distribution increased by ~25% from 1988 to 2016—similar trends were observed for the other heavy metals. This study provides insights into the spatio-temporal trends and the potential causes of soil heavy metal contamination to facilitate appropriate planning and management strategies to prevent, control, and reduce the impact of heavy metal contamination in soils.

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