Energy conservation and emission reduction have become the primary problem that the real economy cannot avoid. Environmental protection will also become the primary condition for manufacturers to choose suppliers. With the temporary closure of over 700 factories, various industries have also ignited a wave of raw material prices. Many companies have been forced to go to the "failed to raise prices" in the case of helpless. Recently, China's continuous heavy fog and haze weather has spread to 17 provinces and 71 cities. The situation of PM2.5 “explosion table†has appeared in many places, which has caused great public concern. It is understood that Beijing temporarily closed more than 700 factories on Monday, and the transportation department adopted measures of single and double rotation restrictions on the travel vehicles to restrict the travel of vehicles. Energy conservation and emission reduction have become the primary problem that the real economy cannot avoid. Environmental protection will also become the primary condition for manufacturers to choose suppliers. With the temporary closure of over 700 factories, various industries have also ignited a wave of raw material prices. It is understood that from the beginning of the year, bulk raw materials such as coal, iron ore and paper will start to rise and will be transmitted to the entire industrial sector in a few months. With the price increase of intermediate products such as packaging and printing, PCB, LED, components, etc., many companies have been forced to "fail to raise prices without price" in the case of helpless. Is industrial pollution really the most important source of haze? Is there a way to predict the arrival of heavyweight ahead of time before eradicating pollution? Xiaobian remembers seeing such a passage in a commentary by Zhou Xiaoping last year: "Many people think that China's environmental pollution control level is not high, so industrial development is the main cause of China's smog. But in fact, this understanding is Not completely rigorous. There are two reasons: First, China's industry does not stop working all year round, and the production capacity is also very balanced. So why in the windless summer, Beijing's blue sky and white clouds are also very common for several weeks, is it summer China? Are the factories all stopped production? Second, why is the fog and sorghum season in autumn and winter? If this is a problem caused by industrial emissions, then we can count the smog cubes in North China, and we can definitely get an amazing Numbers. Don't say China, all humans add up to such a large capacity to emit so much smog gas overnight. Then this shows that the haze has a strong meteorological sporadicity and cannot form a direct causal link with the industrial production level. It must have other reasons. †An environmental engineering professional said that the fog and haze should be said separately. Let's talk about fog first. There is no wind when there is fog. There is no fog when the temperature rises. The fog is related to the air flow. Besides, when there is fog in a local area, it happens to be in the city, and there is no wind, and the pollutants in the city are discharged into the atmosphere. Closely integrated with water molecules. At noon, due to the sun's rays, water molecules evaporate, but there are suspended particles in the water molecules. The water molecules can not be as high as a certain height, can not be blown away, and continue to stay in the local. At this time, pollutants in the city continue to be discharged. So the smog will always burst. The cold wind of West Siberia came, and it was a low air flow, which took away the smog. This is probably some of the smog. In fact, I can assure you here. If the power supply is sufficient, it is possible to establish a grid between the upper levels of the city to achieve the effect of adsorption. Therefore, industry, energy consumption is very important. It is understood that tools developed by IBM and Microsoft conducted air pollution prediction tests in China last year, which integrate a wide range of data sources, such as air pollution monitoring stations, environmental monitoring stations, transportation systems, meteorological satellites, economic data and even social media. It can provide 3 to 10 days of pollution warning. IBM's system can provide forecasts for 3 days at a rate of more than 80%, and forecasts for 7 to 10 days at 75% accuracy. Tests by Microsoft's system in Beijing show that it provides a 6-hour alert with 75% accuracy and a 12-hour alert with 60% accuracy. Atmospherics say how best to combine physics models and machine learning to provide air quality forecasts is an active area of ​​research. In addition to IBM and Microsoft, AirVisual, a startup in Beijing, is also offering enhanced air pollution forecasts using machine learning. Predicting smog requires current air quality data, weather conditions, future weather forecasts, and more. The air quality data does not refer to pure air quality site data, but to the air quality site as the center, covering all air quality related data within a radius of 300 kilometers. Big data predicts that smog depends on a multi-fusion approach. Different from the traditional simulated air quality, the air quality prediction is not only based on the air quality data, but also the data related to different fields such as meteorological data, traffic flow data, factory and mine data, urban road network structure, etc. , complement each other to predict air quality conditions. Since the influence factors of air quality are different at each station and each time period, for each air quality station, it is necessary to collect multi-party data, superimpose and fuse, and finally produce an air quality prediction model. Big data accuracy is 15~20% higher than traditional analog methods Let's look at the traditional simulation method. Because the data obtained in the past is limited, it can only be based on limited sample data. Then researchers can find out the laws between these data through experience and hypothesis, and simulate a simple model to predict air quality. With the increase of data, the vast amount of data can no longer rely on manual to find out the law, so tools such as machine learning and data mining are needed to discover the hidden rules in multi-source data. The comparison shows that the big data prediction is similar to the traditional simulation method to a certain extent, and the model is based on the data, but the size of the data is different from the simulation method. In addition, in the traditional simulation method, it is necessary to collect complete pollution source data for prediction, such as enterprise sewage data and automobile exhaust emissions. However, it is difficult to know all the data, and the actual situation of pollutants spreading and changing in the air. It is much more complicated, so the traditional model is actually a product of idealization. As the factors affecting air quality increase and the amount of data increases, the traditional method of simulation gradually appears to be “powerlessâ€. Big data has an advantage in air quality prediction. The essence is that problems in a certain field can be solved together with data from multiple fields. The problem of data loss and inaccuracy is solved through multi-data fusion. For example, it is necessary to obtain traffic exhaust data. Traditional methods need to obtain accurate pollution source data to make predictions. Big data calculations can use traffic flow, displacement and other related data related to traffic exhaust to indirectly analyze the relationship between air quality and air quality. The problem of missing data. It is worth mentioning that although the red warning has been lifted with the arrival of cold air, the smog has not actually dissipated. If you do not respect the laws of nature, you are not people-oriented, and relying solely on data and intelligence can not cure "urban diseases". No matter how smart the city is, it is only a means of control, and smog will only follow the nightmare. Don't go.
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