A mechanism is provided for forecasting air pollution. One or more air-pollution monitoring stations correlated to a forecasting point from a plurality of air-pollution monitoring stations are identified. For the one or more air-pollution monitoring stations that correlate to the forecasting point, one or more patterns of the forecasting point, historical patterns of the forecasting point relating to the one or more patterns of the forecasting point, and one or more patterns of the air-pollution monitoring stations that relate to the one or more patterns of the forecasting point are identified. Based on the one or more patterns of the forecasting point, the historical patterns of the forecasting point relating to the one or more patterns of the forecasting point, and the one or more patterns of the air-pollution monitoring stations that relate to the one or more patterns of the forecasting point, a pollution forecast is provided.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, in a data processing system comprising a processor and a memory, for forecasting air pollution, the method comprising: identifying, by the data processing system, one or more air-pollution monitoring stations correlated to a forecasting point from a plurality of air-pollution monitoring stations; for the one or more air-pollution monitoring stations that correlate to the forecasting point, identifying, by the data processing system, one or more patterns of the forecasting point, historical patterns of the forecasting point relating to the one or more patterns of the forecasting point, and one or more patterns of the air-pollution monitoring stations that relate to the one or more patterns of the forecasting point; and providing, by the data processing system, a pollution forecast based on the one or more patterns of the forecasting point, the historical patterns of the forecasting point relating to the one or more patterns of the forecasting point, and the one or more patterns of the air-pollution monitoring stations that relate to the one or more patterns of the forecasting point. 2. The method of claim 1, wherein identifying the one or more air-pollution monitoring stations correlated to the forecasting point utilizes air quality data from a plurality of air-pollution monitoring stations and the forecasting point and meteorological data from a plurality of meteorological monitoring stations. 3. The method of claim 1, wherein identifying the one or more air-pollution monitoring stations correlated to the forecasting point comprises: identifying, by the data processing system, a set of air-pollution monitoring stations from the plurality of air-pollution monitoring stations that are within a predetermined distance around the forecasting point; for each of the set of air-pollution monitoring stations, identifying, by the data processing system, a diffusion speed s of pollutant from the air-pollution monitoring station Mi to the forecasting point M utilizing wind speed ws and wind direction wd; computing, by the data processing system, an angle Θ between the monitoring station M1 and the forecasting point M based on the wind direction wd at site Mi; computing, by the data processing system, a velocity of movement v of the pollutant using the diffusion speed s from the monitoring station Mi to the forecasting point M using: v=s+wS*cos Θ; identifying, by the data processing system, a degree of influence Di of the pollutant from the monitoring station Mi to the forecasting point M using: D i = Q 2  π   v   σ y  σ z  exp  [ - 1 2  ( y 2 σ y 2  t + z 2 σ z 2  t ) - k 2  t ] where Q is the pollution value of Mi, v is the velocity of movement, σy is the y-axis diffusion parameter (constant value), αz is the z-axis diffusion parameter (constant value), y is the y-axis distant between Mi and M, z is the z-axis distant between Mi and M, t is the lasting hour (e.g., 1 hour, 2 hours, 3 hours, . . . etc.), and k is the decay factor, which may be a predefined factor; determining, by the data processing system, whether the identified degree of influence Di is greater than a degree of influence threshold DT; and responsive to the degree of influence Di being greater than a degree of influence threshold DT, adding, by the data processing system, the air-pollution monitoring station Mi to the one or more air-pollution monitoring stations correlated to the forecasting point M. 4. The method of claim 1, wherein identifying the historical patterns of the forecasting point relating to the one or more patterns of the forecasting point comprises: identifying, by the data processing system, for the forecasting point M, associated meteorological data and pollution data for a period of time Tp; for the period of time Tp of the forecasting point M, detecting, by the data processing system, the one or more patterns of the forecasting point; extracting, by the data processing system, shape parameters from the one or more patterns of the forecasting point; searching, by the data processing system, for historical pollution data associated with the forecasting point M that has a similar shaped pattern to that of the one or more patterns of the forecasting point thereby forming a set of similar shaped patterns; calculating, by the data processing system, a similarity Sm of each similar shaped pattern in the set of similar shaped patterns; determining, by the data processing system, whether the similarity Sm is greater than a predetermined similarity threshold SmT; and responsive to the similarity Sm being greater than the predetermined similarity threshold SmT, identifying, by the data processing system, from the historical pollution data a historical pollution value ph at time Tp+h. 5. The method of claim 4, wherein the shape parameters comprise one or more of a number of rises, a number of declines, a degree of rises, a degree of declines, an average amplitude, a rise period time length and change range, a decline period length and change range, a maximum detected value, and a minimum detected value. 6. The method of claim 1, wherein identifying the one or more patterns of the air-pollution monitoring stations that relate to the one or more patterns of the forecasting point comprises: identifying, by the data processing system, for each of the one or more air-pollution monitoring stations that correlate to the forecasting point, associated meteorological data and pollution data for a period of time Tp; for the period of time Tp of the forecasting point M, detecting, by the data processing system, the one or more patterns of the forecasting point; extracting, by the data processing system, shape parameters from the one or more patterns of the forecasting point; searching, by the data processing system, for air-pollution monitoring station data of the one or more air-pollution monitoring stations that has a similar shaped pattern to that of the one or more patterns of the forecasting point thereby forming a set of similar shaped patterns; calculating, by the data processing system, a similarity Smi of each similar shaped pattern in the set of similar shaped patterns; determining, by the data processing system, whether the similarity Smi is greater than a predetermined similarity threshold SmT; and responsive to the similarity Smi being greater than a predetermined similarity threshold SmT, identifying, by the data processing system, the air-pollution monitoring station as being associated with an area where the pollution value p may be forecasted to be close to a historical pollution value ph at time Tp+h. 7. The method of claim 1, wherein providing the pollution forecast based on the one or more patterns of the forecasting point, the historical patterns of the forecasting point relating to the one or more patterns of the forecasting point, and the one or more patterns of the air-pollution monitoring stations that relate to the one or more patterns of the forecasting point comprises: for each of the one or more air-pollution monitoring stations, calculating, by the data processing system, a weight wj for a time period Tj according to the similarity Sm at the time period Tj forming Smj and to the similarity Smi at the time period Tj forming Smij using: w j = Smj * Π   Smij ∑ j = 1 n  Smj * Π   Smij ; predicting, by the data processing system, a pollution value pt+h for the one or more air-pollution monitoring stations at time Tp+h using equation: pt+h=Σj=1nwjpj; and outputting, by the data processing system, the pollution forecast to one or more enterprises or individuals in order that action is taken to protect themselves from or to reduce air-pollution. 8. A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to: identify one or more air-pollution monitoring stations correlated to a forecasting point from a plurality of air-pollution monitoring stations; for the one or more air-pollution monitoring stations that correlate to the forecasting point, identify one or more patterns of the forecasting point, historical patterns of the forecasting point relating to the one or more patterns of the forecasting point, and one or more patterns of the air-pollution monitoring stations that relate to the one or more patterns of the forecasting point; and provide a pollution forecast based on the one or more patterns of the forecasting point, the historical patterns of the forecasting point relating to the one or more patterns of the forecasting point, and the one or more patterns of the air-pollution monitoring stations that relate to the one or more patterns of the forecasting point. 9. The computer program product of claim 8, wherein the computer program product to identify the one or more air-pollution monitoring stations correlated to the forecasting point further causes the computing device to utilize air quality data from a plurality of air-pollution monitoring stations and the forecasting point and meteorological data from a plurality of meteorological monitoring stations. 10. The computer program product of claim 8, wherein the computer program product to identify the one or more air-pollution monitoring stations correlated to the forecasting point further causes the computing device to: identify a set of air-pollution monitoring stations from the plurality of air-pollution monitoring stations that are within a predetermined distance around the forecasting point; for each of the set of air-pollution monitoring stations, identify a diffusion speed s of pollutant from the air-pollution monitoring station Mi to the forecasting point M utilizing wind speed ws and wind direction wd; compute an angle Θ between the monitoring station Mi and the forecasting point M based on the wind direction wd at site Mi; compute a velocity of movement v of the pollutant using the diffusion speed s from the monitoring station Mi to the forecasting point M using: v=s+wS*cos Θ; identify a degree of influence Di of the pollutant from the monitoring station Mi to the forecasting point M using: D i = Q 2  π   v   σ y  σ z  exp  [ - 1 2  ( y 2 σ y 2  t + z 2 σ z 2  t ) - k 2  t ] where Q is the pollution value of Mi, v is the velocity of movement, σy is the y-axis diffusion parameter (constant value), σz is the z-axis diffusion parameter (constant value), y is the y-axis distant between Mi and M, z is the z-axis distant between Mi and M, t is the lasting hour (e.g., 1 hour, 2 hours, 3 hours, . . . etc.), and k is the decay factor, which may be a predefined factor; determine whether the identified degree of influence Di is greater than a degree of influence threshold DT; and responsive to the degree of influence Di being greater than a degree of influence threshold DT, add the air-pollution monitoring station Mi to the one or more air-pollution monitoring stations correlated to the forecasting point M. 11. The computer program product of claim 8, wherein the computer program product to identify the historical patterns of the forecasting point relating to the one or more patterns of the forecasting point further causes the computing device to: identify for the forecasting point M, associated meteorological data and pollution data for a period of time Tp; for the period of time Tp of the forecasting point M, detect the one or more patterns of the forecasting point; extract shape parameters from the one or more patterns of the forecasting point; search for historical pollution data associated with the forecasting point M that has a similar shaped pattern to that of the one or more patterns of the forecasting point thereby forming a set of similar shaped patterns; calculate a similarity Sm of each similar shaped pattern in the set of similar shaped patterns; determine whether the similarity Sm is greater than a predetermined similarity threshold SmT; and responsive to the similarity Sm being greater than the predetermined similarity threshold SmT, identify from the historical pollution data a historical pollution value ph at time Tp+h. 12. The computer program product of claim 11, wherein the shape parameters comprise one or more of a number of rises, a number of declines, a degree of rises, a degree of declines, an average amplitude, a rise period time length and change range, a decline period length and change range, a maximum detected value, and a minimum detected value. 13. The computer program product of claim 8, wherein the computer program product to identify the one or more patterns of the air-pollution monitoring stations that relate to the one or more patterns of the forecasting point further causes the computing device to: identify for each of the one or more air-pollution monitoring stations that correlate to the forecasting point, associated meteorological data and pollution data for a period of time Tp; for the period of time Tp of the forecasting point M, detect the one or more patterns of the forecasting point; extract shape parameters from the one or more patterns of the forecasting point; search for air-pollution monitoring station data of the one or more air-pollution monitoring stations that has a similar shaped pattern to that of the one or more patterns of the forecasting point; calculate a similarity Smi of each of similar shaped pattern in the set of similar shaped patterns; determine whether the similarity Smi is greater than a predetermined similarity threshold SmT; and responsive to the similarity Smi being greater than a predetermined similarity threshold SmT, identify the air-pollution monitoring station as being associated with an area where the pollution value p may be forecasted to be close to a historical pollution value ph at time Tp+h. 14. The computer program product of claim 8, wherein the computer program product to provide the pollution forecast based on the one or more patterns of the forecasting point, the historical patterns of the forecasting point relating to the one or more patterns of the forecasting point, and the one or more patterns of the air-pollution monitoring stations that relate to the one or more patterns of the forecasting point further causes the computing device to: for each of the one or more air-pollution monitoring stations, calculate a weight wj for a time period Tj according to the similarity Sm at the time period Tj forming Smj and to the similarity Smi at the time period Tj forming Smij using: w j = Smj * Π   Smij ∑ j = 1 n  Smj * Π   Smij ; predict a pollution value pt+h for the one or more air-pollution monitoring stations at time Tp+h using equation: pt+h=Σj=1nwjpj; and output the pollution forecast to one or more enterprises or individuals in order that action is taken to protect themselves from or to reduce air-pollution. 15. An apparatus comprising: a processor; and a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, cause the processor to: identify one or more air-pollution monitoring stations correlated to a forecasting point from a plurality of air-pollution monitoring stations; for the one or more air-pollution monitoring stations that correlate to the forecasting point, identify one or more patterns of the forecasting point, historical patterns of the forecasting point relating to the one or more patterns of the forecasting point, and one or more patterns of the air-pollution monitoring stations that relate to the one or more patterns of the forecasting point; and provide a pollution forecast based on the one or more patterns of the forecasting point, the historical patterns of the forecasting point relating to the one or more patterns of the forecasting point, and the one or more patterns of the air-pollution monitoring stations that relate to the one or more patterns of the forecasting point. 16. The apparatus of claim 15, wherein the instructions to identify the one or more air-pollution monitoring stations correlated to the forecasting point further cause the processor to utilize air quality data from a plurality of air-pollution monitoring stations and the forecasting point and meteorological data from a plurality of meteorological monitoring stations. 17. The apparatus of claim 15, wherein the instructions to identify the one or more air-pollution monitoring stations correlated to the forecasting point further cause the processor to: identify a set of air-pollution monitoring stations from the plurality of air-pollution monitoring stations that are within a predetermined distance around the forecasting point; for each of the set of air-pollution monitoring stations, identify a diffusion speed s of pollutant from the air-pollution monitoring station Mi to the forecasting point M utilizing wind speed ws and wind direction wd; compute an angle Θ between the monitoring station Mi and the forecasting point M based on the wind direction wd at site Mi; compute a velocity of movement v of the pollutant using the diffusion speed s from the monitoring station Mi to the forecasting point M using: v=s+wS*cos Θ; identify a degree of influence Di of the pollutant from the monitoring station Mi to the forecasting point M using: D i = Q 2  π   v   σ y  σ z  exp  [ - 1 2  ( y 2 σ y 2  t + z 2 σ z 2  t ) - k 2  t ] where Q is the pollution value of Mi, v is the velocity of movement, σy is the y-axis diffusion parameter (constant value), σz is the z-axis diffusion parameter (constant value), y is the y-axis distant between Mi and M, z is the z-axis distant between Mi and M, t is the lasting hour (e.g., 1 hour, 2 hours, 3 hours, . . . etc.), and k is the decay factor, which may be a predefined factor; determine whether the identified degree of influence Di is greater than a degree of influence threshold DT; and responsive to the degree of influence Di being greater than a degree of influence threshold DT, add the air-pollution monitoring station Mi to the one or more air-pollution monitoring stations correlated to the forecasting point M. 18. The apparatus of claim 15, wherein the instructions to identify the historical patterns of the forecasting point relating to the one or more patterns of the forecasting point further cause the processor to: identify for the forecasting point M, associated meteorological data and pollution data for a period of time Tp; for the period of time Tp of the forecasting point M, detect the one or more patterns of the forecasting point; extract shape parameters from the one or more patterns of the forecasting point; search for historical pollution data associated with the forecasting point M that has a similar shaped pattern to that of the one or more patterns of the forecasting point; calculate a similarity Sm of each similar shaped pattern in the set of similar shaped patterns; determine whether the similarity Sm is greater than a predetermined similarity threshold SmT; and responsive to the similarity Sm being greater than the predetermined similarity threshold SmT, identify from the historical pollution data a historical pollution value ph at time Tp+h. 19. The apparatus of claim 18, wherein the shape parameters comprise one or more of a number of rises, a number of declines, a degree of rises, a degree of declines, an average amplitude, a rise period time length and change range, a decline period length and change range, a maximum detected value, and a minimum detected value. 20. The apparatus of claim 15, wherein the instructions to identify the one or more patterns of the air-pollution monitoring stations that relate to the one or more patterns of the forecasting point further cause the processor to: identify for each of the one or more air-pollution monitoring stations that correlate to the forecasting point, associated meteorological data and pollution data for a period of time Tp; for the period of time Tp of the forecasting point M, detect the one or more patterns of the forecasting point; extract shape parameters from the one or more patterns of the forecasting point; search for air-pollution monitoring station data of the one or more air-pollution monitoring stations that has a similar shaped pattern to that of the one or more patterns of the forecasting point; calculate a similarity Smi of each similar shaped pattern in the set of similar shaped patterns; determine whether the similarity Smi is greater than a predetermined similarity threshold SmT; and responsive to the similarity Smi being greater than a predetermined similarity threshold SmT, identify the air-pollution monitoring station as being associated with an area where the pollution value p may be forecasted to be close to a historical pollution value ph at time Tp+h. 21. The apparatus of claim 15, wherein the instructions to provide the pollution forecast based on the one or more patterns of the forecasting point, the historical patterns of the forecasting point relating to the one or more patterns of the forecasting point, and the one or more patterns of the air-pollution monitoring stations that relate to the one or more patterns of the forecasting point further cause the processor to: for each of the one or more air-pollution monitoring stations, calculate a weight wj for a time period Tj according to the similarity Sm at the time period Tj forming Smj and to the similarity Smi at the time period Tj forming Smij using: w j = Smj * Π   Smij ∑ j = 1 n  Smj * Π   Smij ; predict a pollution value pt+h for the one or more air-pollution monitoring stations at time Tp+h using equation: pt+h=Σj=1nwjpj; and output the pollution forecast to one or more enterprises or individuals in order that action is taken to protect themselves from or to reduce air-pollution.