Spatial variability of organochlorine pesticides （DDTs and HCHs） in surface soils from the alluvial region of Beijing, China

The spatial variability in the concentrations of 1,2,3,4,5,6-hexachlorocyclohexane (HCH) and 1,1,1-trichloro-2,2-bis-(p-chlorophenyl) ethane (DDT) in surface soils was studied on the basis of the analysis of 131 soil samples collected from the surface layer (0-20 cm depth) of the alluvial region of Beijing, China. The concentrations of total HCHs (including α-, β-, γ-, and δ-isomers) and total DDTs (i ncluding p,p'-DDT, p,p'-DDD, p,p'-DDE, and o,p'-DDT) in the surface soils tested were in the range from nondetectable to 31.72 μg/kg dry soil, with a mean value of 0.91, and from nondetectable to 5910.83 μg/kg dry soil, with a mean value of 32.13,respectively. It was observed that concentrations of HCHs in all soil samples and concentrations of DDTs in 112 soil samples were much lower than the first grade (50 μg/kg) permitted in "Environment quality standard for soils in China (GB15618-1995)". This suggests that the pollution due to organochlorine pesticides was generally not significant in the farmland soils in the Beijing alluvial region. In this study, the spatial distribution and trend of HCHs and DDTs were analyzed using Geostatistical Analyst and GS (513).Spatial distribution indicated how these pesticides had been applied in the past. Trend analysis showed that the concentrations of HCHs,DDTs, and their related metabolites followed an obvious distribution trend in the surface soils from the alluvial region of Beijing.     

Variability of soil-water quality due to Tsunami-2004 in the coastal belt of Nagapattinam district, Tamilnadu

In this study, the Tsunami-caused deterioration of soil and groundwater quality in the agricultural fields of coastal Nagapattinam district of Tamilnadu state in India is presented by analyzing their salinity and sodicity parameters. To accomplish this, three sets of soil samples up to a depth of 30cm from the land surface were collected for the first six months of the year 2005 from 28 locations and the ground water samples were monitored from seven existing dug wells and hand pumps covering the study region at intervals of 3 months. The EC and pH values of both the soil and ground water samples were estimated and the spatial and temporal variability mappings of these parameters were performed using the geostatistical analysis module of ArcGIS((R)). It was observed that the spherical semivariogram fitted well with the data set of both EC and pH and the generated kriged maps explained the spatial and temporal variability under different ranges of EC and pH values. Further, the recorded EC and pH data of soil and ground water during pre-Tsunami periods were compared with the collected data and generated variability soil maps of EC and pH of the post-Tsunami period. It was revealed from this analysis that the soil quality six months after the Tsunami was nearing the pre-Tsunami scenario (EC< 1.5dSm(-1); pH<8), whereas the quality of ground water remained highly saline and unfit for irrigation and drinking. These observations were compared with the ground scenarios of the study region and possible causes for such changes and the remedial measures for taking up regular agricultural practices are also discussed.     

Spatial distribution of perfluoroalkyl acids in the Pearl River of Southern China

An intensive campaign was conducted in September 2012 to collect surface water samples along the tributaries of the Pearl River in southern China. Thirteen perfluoroalkyl acids (PFAAs), including perfluorocarboxylates (PFCAs, C4–C11) and perfluorosulfonates (PFSAs, C4, C6–C8, and C10), were determined using high-performance liquid chromatography/negative electrospray ionization–tandem mass spectrometry (HPLC/(-)ESI–MS/MS). The concentrations of total PFAAs (ΣPFAAs) ranged from 3.0 to 52 ng L⁻¹, with an average of 19 ± 12 ng L⁻¹. The highest concentrations of ΣPFAAs were detected in the surface water of the Dong Jiang tributary (17–52 ng L⁻¹), followed by the main stream (13–26 ng L⁻¹) and the Sha Wan stream (3.0–4.5 ng L⁻¹). Perfluorooctanoate (PFOA), perfluorobutane sulfonate (PFBS), and perfluorooctane sulfonate (PFOS) were the three most abundant PFAAs and on average accounted for 20%, 24%, and 19% of ΣPFAAs, respectively. PFBS was the most abundant PFAA in the Dong Jiang tributary, and PFOA was the highest PFAA in the samples from the main stream of the Pearl River. A correlation was found between PFBS and PFOA, which suggests that both of these PFAAs originate from common source(s) in the region. Nevertheless, the slope of PFBS/PFOA was different in the different tributaries sampled, which indicates a spatial difference in the source profiles of the PFAAs.     

Spectral estimation of global levels of atmospheric pollutants

Underlying levels of atmospheric pollutants, assumed to be governed by smoothing mechanisms due to atmospheric dispersion, can be estimated from global emissions source databases on greenhouse gases and ozone-depleting compounds. However, spatial data may be contaminated with noise or even missing or zero-valued at many locations. Therefore, a problem that arises is how to extract the underlying smooth levels. This paper sets out a structural spatial model that assumes data evolve across a global grid constrained by second-order smoothing restrictions. The frequency-domain approach is particularly suitable for global datasets, reduces the computational burden associated with two-dimensional models and avoids cumbersome zero-inflated skewed distributions. Confidence intervals of the underlying levels are also obtained. An application to the estimation of global levels of atmospheric pollutants from anthropogenic emissions illustrates the technique which may also be useful in the analysis of other environmental datasets of similar characteristics.     

Field monitoring of volatile organic compounds using passive air samplers in an industrial city in Japan

Highly portable, sensitive, and selective passive air samplers were used to investigate ambient volatile organic compound (VOC) levels at multiple sampling sites in an industrial city, Fuji, Japan. We determined the spatial distributions of 27 species of VOCs in three campaigns: Mar (cold season), May (warm season), and Nov (mild season) of 2004. In all campaigns, toluene (geometric mean concentration, 14.0microg/m3) was the most abundant VOC, followed by acetaldehyde (4.76microg/m3), and formaldehyde (2.58microg/m3). The spatial distributions for certain VOCs showed characteristic patterns: high concentrations of benzene and formaldehyde were typically found along major roads, whereas high concentrations of toluene and tetrachloroethylene (PCE) were usually found near factories. The spatial distribution of PCE observed was extremely consistent with the diffusion pattern calculated from Pollutant Release and Transfer Register data and meteorological data, indicated that passive air samplers are useful for determining the sources and distributions of ambient VOCs.     

Spatiotemporal variations of ambient air pollutants and meteorological influences over typical urban agglomerations in China during the COVID-19 lockdown

To investigate the air quality change during the COVID-19 pandemic, we analyzed spatiotemporal variations of six criteria pollutants in nine typical urban agglomerations in China using ground-based data and examined meteorological influences through correlation analysis and backward trajectory analysis under different responses. Concentrations of PM_2.5, PM₁₀, NO₂, SO₂ and CO in urban agglomerations respectively decreased by 18%–45% (30%–62%), 17%–53% (22%–39%), 47%-64% (14%–41%), 9%–34% (0%–53%) and 16%-52% (23%–56%) during Lockdown (Post-lockdown) period relative to Pre-lockdown period. PM_2.5 pollution events occurred during Lockdown in Beijing-Tianjin-Hebe (BTH) and Middle and South Liaoning (MSL), and daily O₃ concentration rose to grade Ⅱ standard in Post-lockdown period. Distinct from the nationwide slump of NO₂ during Lockdown period, a rebound (～40%) in Post-lockdown period was observed in Cheng-Yu (CY), Yangtze River Middle-Reach (YRMR), Yangtze River Delta (YRD) and Pearl River Delta (PRD). With slightly higher wind speed compared with 2019, the reduction of PM_2.5 (51%–62%) in Post-lockdown period is more than 2019 (15%–46%) in HC (Harbin-Changchun), MSL, BTH, CP (Central Plain) and SP (Shandong-Peninsula), suggesting lockdown measures are effective to PM_2.5 alleviation. Although O₃ concentrations generally increased during the lockdown, its increment rate declined compared with 2019 under similar sunlight duration and temperature. Additionally, unlike HC, MSL and BTH, which suffered from additional (> 30%) air masses from surrounding areas after the lockdown, the polluted air masses reaching YRD and PRD mostly originated from the long-distance transport, highlighting the importance of joint regional governance.     

大连市东部地区土地利用及土地覆盖的空间格局研究

为了判明大连市东部地区景观空间结构随开发过程所发生的定量变化,根据景观生态学的原理,选择大连市东部代表地区庄河市1988年和2002年的资料进行分析比较,对庄河市区域景观空间格局的动态变化进行了分析,定量分析其土地利用、覆盖等,对在土地开发利用、保护、治理、开发建设及布局、土地管理、发展旅游等方面具有指导意义。     

Planning nature-based solutions: Principles,steps, and insights

Nature-based solutions (NBS) find increasing attention as actions to address societal challenges through harnessing ecological processes, yet knowledge gaps exist regarding approaches to landscape planning with NBS. This paper aims to provide suggestions of how planning NBS can be conceptualized and applied in practice. We develop a framework for planning NBS by merging insights from literature and a case study in the Lahn river landscape, Germany. Our framework relates to three key criteria that define NBS, and consists of six steps of planning: Co-define setting, Understand challenges, Create visions and scenarios, Assess potential impacts, Develop solution strategies, and Realize and monitor. Its implementation is guided by five principles, namely Place-specificity, Evidence base, Integration, Equity, and Transdisciplinarity. Drawing on the empirical insights from the case study, we suggest suitable methods and a checklist of supportive procedures for applying the framework in practice. Taken together, our framework can facilitate planning NBS and provides further steps towards mainstreaming.Electronic supplementary materialThe online version of this article (10.1007/s13280-020-01365-1) contains supplementary material, which is available to authorized users.     

Variation of MCPA, metribuzine, methyltriazine-amine and glyphosate degradation, sorption, mineralization and leaching in different soil horizons

Pesticide mineralization and sorption were determined in 75 soil samples from 15 individually drilled holes through the vadose zone along a 28 km long transect of the Danish outwash plain. Mineralization of the phenoxyacetic acid herbicide MCPA was high both in topsoils and in most subsoils, while metribuzine and methyltriazine-amine was always low. Organic matter and soil pH was shown to be responsible for sorption of MCPA and metribuzine in the topsoils. The sorption of methyltriazine-amine in topsoil was positively correlated with clay and negatively correlated with the pH of the soil. Sorption of glyphosate was tested also high in the subsoils. One-dimensional MACRO modeling of the concentration of MCPA, metribuzine and methyltriazine-amine at 2 m depth calculated that the average concentration of MCPA and methyltriazine-amine in the groundwater was below the administrative limit of 0.1 μg/l in all tested profiles while metribuzine always exceeded the 0.1 μg/l threshold value.     

Outlier identification and visualization for Pb concentrations in urban soils and its implications for identification of potential contaminated land

Outliers in urban soil geochemical databases may imply potential contaminated land. Different methodologies which can be easily implemented for the identification of global and spatial outliers were applied for Pb concentrations in urban soils of Galway City in Ireland. Due to its strongly skewed probability feature, a Box–Cox transformation was performed prior to further analyses. The graphic methods of histogram and box-and-whisker plot were effective in identification of global outliers at the original scale of the dataset. Spatial outliers could be identified by a local indicator of spatial association of local Moran's I, cross-validation of kriging, and a geographically weighted regression. The spatial locations of outliers were visualised using a geographical information system. Different methods showed generally consistent results, but differences existed. It is suggested that outliers identified by statistical methods should be confirmed and justified using scientific knowledge before they are properly dealt with.