Design of a Spark Big Data Framework for PM2.5 Air Pollution Forecasting.

In recent years, with rapid economic development, air pollution has become extremely serious, causing many negative effects on health, environment and medical costs. PM2.5 is one of the main components of air pollution. Therefore, it is necessary to know the PM2.5 air quality in advance for health. Many studies on air quality are based on the government's official air quality monitoring stations, which cannot be widely deployed due to high cost constraints. Furthermore, the update frequency of government monitoring stations is once an hour, and it is hard to capture short-term PM2.5 concentration peaks with little warning. Nevertheless, dealing with short-term data with many stations, the volume of data is huge and is calculated, analyzed and predicted in a complex way. This alleviates the high computational requirements of the original predictor, thus making Spark suitable for the considered problem. This study proposes a PM2.5 instant prediction architecture based on the Spark big data framework to handle the huge data from the LASS community. The Spark big data framework proposed in this study is divided into three modules. It collects real time PM2.5 data and performs ensemble learning through three machine learning algorithms (Linear Regression, Random Forest, Gradient Boosting Decision Tree) to predict the PM2.5 concentration value in the next 30 to 180 min with accompanying visualization graph. The experimental results show that our proposed Spark big data ensemble prediction model in next 30-min prediction has the best performance (R2 up to 0.96), and the ensemble model has better performance than any single machine learning model. Taiwan has been suffering from a situation of relatively poor air pollution quality for a long time. Air pollutant monitoring data from LASS community can provide a wide broader monitoring, however the data is large and difficult to integrate or analyze. The proposed Spark big data framework system can provide short-term PM2.5 forecasts and help the decision-maker to take proper action immediately.

Sensitivity of a tropical micro-crustacean (Daphnia lumholtzi) to trace metals tested in natural water of the Mekong River.

Metal contamination is one of the major issues to the environment worldwide, yet it is poorly known how exposure to metals affects tropical species. We assessed the sensitivity of a tropical micro-crustacean Daphnia lumholtzi to three trace metals: copper (Cu), zinc (Zn) and nickel (Ni). Both, acute and chronic toxicity tests were conducted with metals dissolved in in situ water collected from two sites in the lower part of the Mekong River. In the acute toxicity test, D. lumholtzi neonates were exposed to Cu (3-30µgL-1), Zn (50-540µgL-1) or Ni (46-2356µgL-1) for 48h. The values of median lethal concentrations (48h-LC50) were 11.57-16.67µg Cu L-1, 179.3-280.9µg Zn L-1, and 1026-1516µg Ni L-1. In the chronic toxicity test, animals were exposed to Cu (3 and 4µgL-1), Zn (50 and 56µgL-1), and Ni (six concentrations from 5 to 302µgL-1) for 21days. The concentrations of 4µg Cu L-1 and 6µg Ni L-1 enhanced the body length of D. lumholtzi but 46µg Ni L-1 and 50µg Zn L-1 resulted in a strong mortality, reduced the body length, postponed the maturation, and lowered the fecundity. The results tentatively suggest that D. lumholtzi showed a higher sensitivity to metals than related species in the temperate region. The results underscore the importance of including the local species in ecological risk assessment in important tropical ecosystems such as the Mekong River to arrive at a better conservational and management plan and regulatory policy to protect freshwater biodiversity from metal contamination.