20240710 22341600 1.0 TRUE <DIV STYLE="text-align:Left;"><DIV><P><SPAN>This is a proposed set of the lakes assessed by MPCA's surface water quality assessment process for the 2018 303(d)/305(b) integrated report to EPA. EPA approved the data without requesting any changes in January, 2019. These lakes are a subset and enhancement of the 1:24,000 scale National Hydrography Dataset (NHD). In cases where the NHD does not include lakes that MPCA has assessed, additional lakes from the MN Department of Natural Resources (DNR) Division of Waters lakes database are added. Since the assessed lakes are a small subset of the NHD and DNR lakes, only that subset is included in the dataset. This dataset includes impaired lakes which have not yet had a TMDL plan approved by the US EPA as well as lakes that support aquatic recreation and those that had insufficient information to render a support judgment.</SPAN></P></DIV></DIV> inlandWaters surface water water chemistry water quality These data are intended to be used for planning, informational, and cartographic purposes related to surface water quality in Minnesota. Assessed Lakes, Minnesota, 2018 (Final) /9j/4AAQSkZJRgABAQEAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCADIASwDASIA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iii gAooooAKKKKACiimOVxy2PxoAfRUULFlO45INS0oyUldAFFFFMAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKrM3mEFV5x0qzRis 6kOdWvoBBEfnI9RU9MCKG3Ac0+inFxjYAooorQAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigApvO7ttxTqKACiimswUZJxQA6iovPX0OKl qYyUtmAUUUVQELxhfmAJHoDQrMpAySPdampDzU8uoACGGR0paj2cYB4z0HFCgoTjJH1p3AkopAcj NBIHWmAtFMR94JxgduadkDvQAtFICD0INLQAUUhOBk1C8pPCfnUymorUCeiqZySd36809ZGXvkeh rFYhXs1YCzRUayq3HQ+hqSt001dAFFFFMAooooAKKKKACiiigAooooAKKKKACiiigAopCcdqWgAq oxZ/vZ+npVuk6e5rOpDnVr2AhjQEZK8g8danpu7PI6UrYAyegpwioqyAWik3AjPpRww9Qaq4CnkV GRsXgsec+tSUU7ARecu4Dj0OaUn5sEE/0pWjViCQDihgexHuDU6gRgNjcCefWnKuTkgk4xk08KAB 3IHWlxRYBjYK4xuAoES4GRk0rMFGWx7VE0rE8ce/eolUjDcCUBIwT096jeUk/LwPWozyck5PvRXL PEN6R0AcWLkBmwKTOCdnA9e5pKKz9rK3n3GFFFFZgFOWRl75HoabRVRm4u6AnWZW4PB9DUlVKcrs vQ8ehrqhiekhFmio1lVuDwfepK6lJSV0AUUUUwCiiigAooooAKKKKACiiigAooppP6/pQA6kNAOa M4HOKQEMhZEGBuPQn1pZHZSqqOv406RygzjPNRo5L/MeMfhWcmr8twJEXkFj82ORSg7jx93qDTuD TducgjHNXYB3t3paQDnNLVAFIemM80HmgAAADoKADFNdwi5PPoKVmCjJNV2Yu2T+A9Kxq1VBeYAS WOWPNJRRXnttu7GFFFFIAoopVUu2BTSbdkAKpZsDrUpRI1y3J96eqqgwPzquzb23du1dbjGjG71Y hCc9lH0GKKKK5G23djCiiikAU5XZeh49DTaKcZOLugJ1lVuDwfepKqU5XZeh49DXXDE9JCLNFMRw 4zjGOtPrqTTV0AUUUUwCiiigAooooATOKRicYHX2pJBlDxnHNIp5BxjIyc9am+tgHDP0OOlDA54N AOc8EYp1MBjAkAgj8abtbccBQCOo9alqORmXG3b1xzSaSAcOrf4U6kAxR7UwFopiKVXBOfen00A1 uVIFMLLGgHOccDvUtQtESxYN19aifMleK1AjZixyaQAnoCfpUywgfeJNSAADAGBXNHDyk7zYFd0K LliOe1MHY1LKpdwADx3PSmbWBxtP5VNWFnaKASlAJ6An8KesJP3jgegqYAAYFFPDN6yAgWJmPPAq ZVVR8oAp1FdUKcYbARyZ2HHXimrAM5Y5+lTUU3BN3YELQjHy9feovardRSx5+ZRzWNagmrx3Ahop 3lvgnb+Zpu1+PlPPtXL7KfYYU4K3901MkYTnqfWn10Qwyt7wiuI3PYD6mnNEqxk8kgetTUhAIIPQ 1tGjCOyAgjbY3PQ1YqPyUz3+makooxlGNpAFFFFagFFFFABRRRQAVDIcEMCN3AwalPNMzlsYz79h Uy10AVeF65H1p9N3DO3Iz6U6mgCoTGzOSxBXPSpSMkckUtDVwGM20+31oVw+cHpQ6blwO3SmRKwY kjAIrNuSklbQCak79KWitQCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigApqtuGe3b3pHAOM8jPSn0gCiikJx1pgMA+fdjBxjHrTgSPvHqeKRQckkAZ9O9 PqUAgORkY/ClpKWqQBRRRQAg6DrSjkUh6jmloAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKAEIyMGkAK5ycr706igBOc+1LTCMZKjn+dPoAQjNNLAYzxk 460+mEDd05PekwHcn2FBOKPwoHPOKAF96KaQD16dadQAmaBk8/lSEnt+VAwCR360XAdRRRTAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA Q8/Sloo70AIc8Ypqrtz8xJPrT6KVgDoKb/Hn2pegpaYBRTGbaQCDg96fQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUh/XtS0UAFFFIAASfWgBaKKKACmgtu IK8djmnY5ooAM84ppPBPXHpSkAjmlpAFFFFMBpAYEEZBpVUKMDpS0UrAFFFFMAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoopO45oAQnBAx1706joKaDkA4x 9aAHUUnfGOKWgAooooAKKB0ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAP/9k=