ArticleSN applied sciences2021
Big data challenges in overcoming China's water and air pollution: relevant data and indicators.
Article in SN applied sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Fish and macroinvertebrate assemblages reveal extensive degradation of the world's rivers.Global change biology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Big data are potentially useful for environmental management planning and actions that can be directed toward pollution control. China is using big data approaches to help reduce its current levels of pollution. However, also needed are better environmental indicators, measurement technologies, data management and reporting, and adaptive management and enforcement. Based on continental-extent monitoring and assessment programs in Europe and the USA, we recommend three major programmatic changes for China. (1) Establish long-term systemic environmental and human health objectives and indicators. (2) Adopt national standard methods for survey designs, sampling and analytical protocols, statistical analyses, and collaborative sampling programs. (3) Provide a transparent process for reporting and correcting data errors.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.