To improve the efficiency and accuracy of water quality model parameter calibration and avoid local optima and the phenomenon in which different parameters have the same effect, this paper proposed a novel Bayesian-based water quality model parameter calibration method. Using Bayesian inference, the parameter calibration problem was converted into a posterior probability function sampling problem, which was sampled using the Markov Chain Monte Carlo algorithm. The convergence speed of the calibration was further improved by setting the optimized initial sampling value. The influences of the initial sampling value, Markov chain length, and proposal distribution form on the calibration effect were evaluated using four specific cases. The results indicate that (1) the mean relative error (MRE) of the parameter calibration results of this method is less than 10%, with the calibration MRE of Dx and Dy being 5.3% and 8.3%, respectively; (2) when the parameter sensitivity is low, the calibration effect of this method is relatively poor, with a calibration MRE of 46% for k; (3) the parameter calibration can be completed more efficiently by setting an optimized initial value for the MCMC, choosing a reasonable Markov chain length and a suitable proposal distribution form. PRACTITIONER POINTS: Bayesian-based water quality model parameter calibration method is proposed and posterior probability distribution was sampled using the MCMC algorithm. Parameter calibration can be completed more efficiently by setting an optimized initial value for the MCMC. As a result, efficient and accurate parameter calibration of water quality models was achieved. This method is widely applicable to various models, and the calibration speed depends on the calculation speed of the model.
Iron ion is the common transition metal ion in atmospheric aerosol, which can affect the components and optics of secondary organic aerosol (SOA). In the current study, the atmospheric photooxidation of toluene to produce SOA in the presence of ferric chloride fine particles is simulated in a smog chamber; on-line and off-line mass spectrometry and spectroscopic instruments are used to characterize constituents and optics of SOA. Compare with SOA formed in the absence of fine particles, the laser desorption/ionization mass spectra of toluene SOA generated in the presence of ferric chloride fine particles show ion peaks of m/z = 163 and 178, the UV-Vis spectra of the extracting solution for toluene SOA have peaks near 400 and 700 nm, and the electrospray ionization mass spectra contain peaks at m/z = 248 and 300. Based on this spectral information, it is shown that gaseous methylcatechol formed from photooxidation of toluene may react with iron ion on the surface of fine particles by complexing and oxidation–reduction, resulting in methylbenzoquinone products and metallo-organic complex ions such as [Fe(III)(CH3C6H3OO)]+, [Fe(III)(CH3C6H3 OO)2]− and [Fe(III)(CH3C6H3OO)Cl2]−. These products have strong light absorption ability, resulting in an increase in the averaged mass absorption coefficient () in the 200~1000 nm range and the MAC at 365 nm (MAC365) for toluene SOA, while and MAC365 progressively increase with an increasing concentration of ferric chloride fine particles. These results serve as experimental references for the study of the formation mechanism and optical properties of metallo-organic complexes in atmospheric aerosol particles in regions experiencing high levels of fine particles of metal and automobile exhaust pollution.
水中亚硝酸根(NO2-)能在紫外光(UV)激发下产生羟基自由基(·OH)及氮氧活性物质(RNs),促进有机污染物降解;然而该反应过程中的共存因子影响、氧化组分相关关系及副产物毒理学等方面研究比较有限.基于此,本文采用UV/NO2-体系对典型新污染物(ECs)进行降解,并研究体系反应机制和新污染物环境归趋.实验结果表明,UV/NO2-体系对双酚类、磺胺类、非甾体类、氟喹诺酮类和染料等14种新污染物都具有高效的处理效果,且具有一定浓度依赖关系.以双酚F(BPF)为目标污染物,实验发现在UV/NO2-体系中硝酸根具有明显协同促进作用;腐殖酸、亚铁离子和铜离子通过光掩蔽和猝灭活性物质从而与UV/NO2-体系产生拮抗作用;海水、二沉池水和珠江水等水质对体系活性具有较高的抑制作用.随着pH增大,BPF降解速率显著提高;酸性条件下以·OH和RNs为主要贡献,碱性条件下以单独UV光解为主;·OH和RNs贡献率之间存在显著相关关系.BPF在UV/NO2-体系中降解路径包括羟基化反应和亚硝化反应,生成的亚硝化产物具有一定的毒理学风险;实验发现通过延长反应时间可以基本消除BPF及其转化产物,使得BPF反应液达到深度矿化程度.本研究相关结果有助于深入认识UV/NO2-体系的活性机制,同时为UV处理水中痕量新污染物提供基础理论支撑.
河道沉积物重金属对河底生态环境质量有着深远的影响.太湖流域河网纵横,人水关系密切,河网环境质量受到人类活动的影响.为掌握河网表层沉积物重金属空间分布特征与强度,本研究以太湖流域嘉善县纵横交错的河网为研究对象,使用潜在生态风险指数评价法(the Potential Ecological Risk Index,PERI)对51个点位的Hg、Cd、Cu等9种重金属的单因子及综合生态风险水平进行评估.通过相关性分析和方差分解表征沉积物重金属对于细菌群落整体的影响.为研究沉积物细菌群落对重金属生态风险指数的响应特征,基于16SrDNA扩增子高通量测序获得的群落信息,使用临界指示物种分析法(Threshold Indicator Taxa Analysis,TITAN)建立群落与风险指数的定量响应关系.研究发现,此区域15.69％点位的沉积物存在重金属"中等"及以上生态风险;Cd、Ni、Pb显著影响细菌群落的α-多样性.定量分析表明,群落中共441个生物序列可操作分类单元(Operational taxonomic units,OTUs)对综合生态风险指数存在有效响应,234个OTUs对一种或一种以上单因子风险指数存在有效响应.当综合风险指数分别为80.78与106.26,群落中正、负响应物种类群丰度发生最大幅度的变化.沉积物重金属生态风险主要来自于Hg和Cd.有97个OTUs分别对Hg或Cd的生态风险有预警及指示意义,其中,属于Anaerolineales或Burkholderiales菌目的较多.本研究创新性地将PERI法和高通量测序技术结合到TITAN法中,研究了细菌群落与重金属生态风险指数的定量响应关系,为重金属污染生态风险监测、评估提供了新的思路.
以对乙酰氨基酚(Acetaminophen,AAP)为目标污染物,结合降解动力学、密度泛函理论(Density Functional Theory,DFT)计算及毒性模拟,从有机和无机两个方面研究了 UV/PDS氧化AAP过程中Br的转化规律.结果表明,低浓度的Br-(≤0.1mmol·L-1)使AAP的降解速率从0.0895 min-1降至0.0645 min-1,而高浓度的Br-(0.5～2mmol·L-1)使其增至0.1052～0.1583 min-1;与溴相关的物种对AAP降解的贡献率达到46.80％.低PDS浓度、低pH值、低AAP浓度和高Br-浓度会促进Br-向HOBr转化.AAP浓度对HOBr转化为溴酸盐(BrO3-)和总有机溴(Total Organic Bromine,TOBr)的影响显著.TOBr的形成是HOBr、活性溴物种(RBS,如Br2*-)和自由基(如SO4*-和OH*)联合作用于AAP及其中间产物的结果.此外,通过DFT计算和HPLC/MS分析,提出了 Br-转化机制和AAP降解途径.毒性模拟表明,形成的大部分溴代有机物的毒性高于AAP的毒性.
