基于SPAMS的天津市夏季环境受体中颗粒物的混合状态及来源

天津位于京津冀区域,近年来面临的颗粒物污染问题受到广泛关注,研究其大气环境中颗粒物的化学组成及来源具有重要意义.为明确天津市夏季环境受体中颗粒物的混合状态及可能来源,于2017年7月利用单颗粒气溶胶质谱仪(single particle aerosol mass spectrometer,SPAMS)在津南区采集到成功电离有粒径及完整质谱信息颗粒209 887个,利用ART-2a对有质谱数据的颗粒按照质谱特征的相似性进行聚类共获得369个颗粒物类别,随后按照类别的化学组成(质谱谱图)的相似性进行人工合并获得19个颗粒物类别,包括:K-EC(0.20%)、K-EC-Sec(0.18%)、K-NO_3-PO_3(12.00%)、K-NO_3-SiO_3(2.98%)、K-Sec(0.16%)、EC(39.60%)、EC-Sec(3.46%)、EC-HM-Sec(3.93%)、HEC(1.49%)、HEC-Sec(1.38%)、OC-Amine-Sec(3.58%)、OC-Sec(0.36%)、OCEC-Sec(0.71%)、Dust-HEC(21.35%)、Dust-Sec(0.72%)、Cl-EC-NO_3(1.22%)、Na-Cl-NO_3(3.20%)、HM-Sec(2.58%)和PAH-Sec(0.90%)颗粒.得到的各个颗粒类别可归因于气溶胶颗粒的不同来源及不同的传输和反应过程,综合分析采集到的颗粒贡献源主要包括机动车排放源、生物质燃烧源、工业排放源、扬尘源、燃煤源和二次源等.其中K-EC、EC、HEC和Dust-HEC等颗粒主要来自一次源直接排放,K-Sec、OC-Amine-Sec、OC-Sec、OCEC-Sec和Na-Cl-NO_3等颗粒大都是一次源排放颗粒经历了不同程度的老化或与二次组分进行了不同程度的混合.     

Atmospheric Deposition on Swiss Long-Term Forest Ecosystem Research (LWF) Plots

Atmospheric deposition of the major elements was estimated from throughfall and bulk deposition measurements on 13 plots of the Swiss Long-Term Forest Ecosystem Research (LWF) between 1995 and 2001. Independent estimates of the wet and dry deposition of nitrogen (N) and sulfur (S) on these same plots were gained from combined simplified models. The highest deposition fluxes were measured at Novaggio (Southern Switzerland), exposed to heavy air pollution originating from the Po Plain, with throughfall fluxes averaging 29 kg ha^–1 a^–1 for N and 15 kg ha^–1 a^–1 for S. Low deposition fluxes were measured on the plots above 1800 m, with throughfall fluxes lower than 4.5 kg ha^–1 a^–1 for N and lower than 3 kg ha^–1 a^–1 for S. The wet deposition of N and S derived from bulk deposition was close to the modeled wet deposition, but the dry deposition derived from throughfall was significantly lower than the modeled dry deposition for both compounds. However, both the throughfall method and the model yielded total deposition estimates of N which exceeded the critical loads calculated on the basis of long-term mass balance considerations. These estimates were within or above the range of empirical critical loads except above 1800 m.     

Hierarchical Bayesian space-time models

Space-time data are ubiquitous in the environmental sciences. Often, as is the case with atmo- spheric and oceanographic processes, these data contain many different scales of spatial and temporal variability. Such data are often non-stationary in space and time and may involve many observation/prediction locations. These factors can limit the effectiveness of traditional space- time statistical models and methods. In this article, we propose the use of hierarchical space-time models to achieve more flexible models and methods for the analysis of environmental data distributed in space and time. The first stage of the hierarchical model specifies a measurement- error process for the observational data in terms of some 'state' process. The second stage allows for site-specific time series models for this state variable. This stage includes large-scale (e.g. seasonal) variability plus a space-time dynamic process for the anomalies'. Much of our interest is with this anomaly proc ess. In the third stage, the parameters of these time series models, which are distributed in space, are themselves given a joint distribution with spatial dependence (Markov random fields). The Bayesian formulation is completed in the last two stages by speci- fying priors on parameters. We implement the model in a Markov chain Monte Carlo framework and apply it to an atmospheric data set of monthly maximum temperature.     

Markov Chain Monte Carlo on optimal adaptive sampling selections

Under a Bayesian population model with a given prior distribution, the optimal sampling strategy with a fixed sample size n is an n-phase adaptive one. That is, the selection of the next sampling units should sequentially depend on the information obtained from the previously selected units, including the observed values of interest. Such an optimal strategy is in general not executable in practice due to its intensive computation. In many survey sampling situations, an important problem is that one would like to select a set of units in addition to a certain number of sampling units which have been observed. If the optimal strategy is an adaptive one, the selection of the additional units should take both the labels and the observed values of the already selected units into account. Hence, a simpler optimal two-phase adaptive sampling strategy under a Bayesian population model is proposed in this article for practical interest. A Markov chain Monte Carlo method is used to approximate the posterior joint distribution of the unobserved population units after the first phase sampling, for the optimal selection of the second phase sample. This approximation method is found to be successful to select the optimal second-phase sample. Finally, this optimal strategy is applied to a set of data from a study of geothermal CO₂ emissions in Yellowstone National Park as a practical illustrative example.     

Bayesian predictive inference for the proportion of eroded land in a small area in Iowa

There is an increasing interest in the quality of soil, especially for small geographical areas. We present a method to estimate the percent of the area in a county or hydrological basin that is eroded. There are sample data (for several counties in eastern Iowa) from the National Resources Inventory and population data on land use, land capability class, rainfall and slope length and steepness. Using the Gibbs sampler we perform Bayesian predictive inference to obtain estimates for the non-sampled units. These estimates, together with the sample data, provide an estimate of the proportion of the total area that is eroded. We assess the quality of fit of our model using two cross-validation exercises and graphical methods.     

包头市昆河流域环境综合整治应延续

受昆河城区段景观建设工程的启示,在昆河环境综合整治的基本框架的基础上,对全流域的整治和生态系统稳定提出可操作性方案,并论证该系统工程带来的效益和深远意义。     

A Hierarchical Model for Estimating Long‐Term Trend of Atrazine Concentration in the Surface Water of the Contiguous U.S.

Atrazine is a herbicide frequently detected in both surface and groundwater in the United States (U.S.), but its spatiotemporal distribution and concentration trends have only been analyzed recently at regional or local scales. We employed a Bayesian hierarchical modeling approach to assess spatial and seasonal variation in atrazine concentration trends between 1990 and 2010 for the contiguous U.S. A Markov chain Monte Carlo simulation algorithm was used to address the problem of left‐censored data (i.e., atrazine concentration values below method reporting levels). We observed opposing temporal trends in the northern (flat or decreasing) and southern (increasing) regions of the U.S. This spatial variation in temporal trends can be partially explained by the relative amount of cropland in the region. Flat or decreasing trends in the north are more likely in regions with high cropland coverage while positive trends in the south are more likely in regions with low cropland coverage.     

基于概率模型的农地转用外部性测算——以天津市静海区为例

准确地量化农地转用外部性能够为促进外部性内化、建设用地聚集的政策提供参考。论文对农地转用外部性界定和识别进行了理论分析,利用农地转用概率模型估算天津市静海区2006—2014年农地转用的外部性,分析地块特征对农地转用概率的影响,讨论了外部性与建设用地聚集的关系。针对静海区2006—2014年数据的分析结果表明,每有1 hm²农地转用为商住用地,周边0~200、200~400、800~1 600 m范围农地受外部性影响分别为0.91×10⁴、0.91×10⁴、0.33×10⁴ 元/hm²,每有1 hm²农地转用为住宅用地,周边800~1 600 m范围内农地受外部性影响为0.18×10⁴ 元/hm²。农地转用正外部性偏小,聚集效应不足,可能是建设用地分散的重要原因,论文还对外部性测算方法以及地块特征的农地转用影响进行了讨论。     

太湖流域肥料施用策略调整对典型作物系统氮磷流失的影响

太湖流域种植业肥料施用强度普遍较高，且以化学肥料为主要形态.因肥料投入不适宜，种植业氮、磷流失问题显著.2015年以来，各地区积极对种植业肥料施用策略进行调整，但当前工作主要基于粮食作物系统且仍停留在化肥施用总量削减和有机肥施用面积提升层面上，缺少菜地、果园、茶园作物系统的相关数据以及对农业环境问题的响应.对此，以苏州市吴中区为太湖流域典型农区代表，研究2019～2021年稻田、菜地、果园和茶园这4类作物系统肥料策略调整对氮、磷流失的影响.结果表明，肥料源养分投入强度的调控是决定氮、磷流失的关键；适宜的有机肥替代比例有助于降低氮、磷流失风险，但有机肥施用需考虑时机并尽可能搭配农用机械.肥料效率是兼顾农业生产过程环境友好、生产主体经济效益的核心，也是后期肥料施用策略调整的导向.稻田系统的肥料施用策略调整应重视养分中不同元素配比，菜地系统应以种植结构调整为抓手，茶园、果园系统可从复合系统视角制定同时满足茶、果生长的施肥策略，助力构建满足农业绿色发展需求的作物系统.