Five species of the genus Labiobaetis (Baetidae) are recognized from China based on mature nymphs, including a new species, Labiobaetis ancoralis n. sp. and a new combination, Labiobaetis mustus (Kang & Yang) n. comb. The new species differs from L. multus (Müller-Liebenau) and L. pulchellus (Müller-Liebenau & Hubbard) by the spatulate submarginal setae on labrum and the apical segment of maxillary palpus with a pronounced excavation near apex. Imago stage of L. mustus (Kang & Yang) n. comb. is described for the first time based on male and female imagoes reared from nymphs, which can be distinguished from other known imagoes of Labiobaetis by the color pattern of abdominal terga and hind wing having a tiny vestigial costal process. All Oriental species previously transferred to Pseudocloeon are reassigned to Labiobaetis in the present paper. An identification key is provided to the known mature nymphs of Oriental Labiobaetis species.

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A new species of the monogeneric family Prosopistomatidae, Prosopistoma ocellatum sp. n., is described and illustrated based on mature larval stages from Guangxi and Hainan, southern China. The new species can be readily distinguished from the other members of Prosopistoma by the following combination of characters: antenna 6-segmented, segment III much longer than segments IV–VI; three bristles at the base of the inner canine; 10–11 pectinate setae on ventral margin of fore tibiae, ventral and basal half surface of all femora with dense scale-like structures, and color pattern of eye-spot on the mesonotum. An update key to the known Oriental species is provided.

Reducing human vulnerability to chemical hazards in the industrialized city is a matter of great urgency. Vulnerability mapping is an alternative approach for providing vulnerability-reducing interventions in a region. This study presents a method for mapping human vulnerability to chemical hazards by using clustering analysis for effective vulnerability reduction. Taking the city of Shanghai as the study area, we measure human exposure to chemical hazards by using the proximity model with additionally considering the toxicity of hazardous substances, and capture the sensitivity and coping capacity with corresponding indicators. We perform an improved k-means clustering approach on the basis of genetic algorithm by using a 500 m × 500 m geographical grid as basic spatial unit. The sum of squared errors and silhouette coefficient are combined to measure the quality of clustering and to determine the optimal clustering number. Clustering result reveals a set of six typical human vulnerability patterns that show distinct vulnerability dimension combinations. The vulnerability mapping of the study area reflects cluster-specific vulnerability characteristics and their spatial distribution. Finally, we suggest specific points that can provide new insights in rationally allocating the limited funds for the vulnerability reduction of each cluster.

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